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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-11-12
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">177</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">20</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
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                <span id="display-count" class="font-medium">显示 177 篇论文 (共 177 篇)</span>
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08378v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>告别跷跷板效应：通过混合意图的双重约束实现精准的长尾会话推荐
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiao Wang, Ke Qin, Dongyang Zhang, Xiurui Xie, Shuang Liang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究长尾会话推荐中准确性与多样性之间的权衡冲突问题，核心思想是通过混合意图学习和意图约束损失来识别并约束会话无关噪声，将传统的“跷跷板”效应转化为“双赢”结果。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统中的长尾分布问题，提出了混合意图双约束框架来解决准确性与多样性之间的权衡冲突，与推荐系统核心领域进展高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:00:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08378v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08378v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In the practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that severely compromises recommendation diversity. Existing approaches attempt to address this issue by promoting tail items but incur accuracy degradation, exhibiting a "see-saw" effect between long-tail and accuracy performance. We attribute such conflict to session-irrelevant noise within the tail items, which existing long-tail approaches fail to identify and constrain effectively. To resolve this fundamental conflict, we propose \textbf{HID} (\textbf{H}ybrid \textbf{I}ntent-based \textbf{D}ual Constraint Framework), a plug-and-play framework that transforms the conventional "see-saw" into "win-win" through introducing the hybrid intent-based dual constraints for both long-tail and accuracy. Two key innovations are incorporated in this framework: (i) \textit{Hybrid Intent Learning}, where we reformulate the intent extraction strategies by employing attribute-aware spectral clustering to reconstruct the item-to-intent mapping. Furthermore, discrimination of session-irrelevant noise is achieved through the assignment of the target and noise intents to each session. (ii) \textit{Intent Constraint Loss}, which incorporates two novel constraint paradigms regarding the \textit{diversity} and \textit{accuracy} to regulate the representation learning process of both items and sessions. These two objectives are unified into a single training loss through rigorous theoretical derivation. Extensive experiments across multiple SBR models and datasets demonstrate that HID can enhance both long-tail performance and recommendation accuracy, establishing new state-of-the-art performance in long-tail recommender systems.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08181v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>MARC：面向冷启动推荐系统的多模态与多任务代理检索增强生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seung Hwan Cho, Yujin Yang, Danik Baeck, Minjoo Kim, Young-Min Kim, Heejung Lee,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究冷启动推荐系统问题，核心思想是构建基于图数据库的多模态多任务Agentic RAG框架，通过任务识别路由器和反思过程生成高质量上下文相关推荐。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接结合LLM的Agent概念与多模态RAG技术解决冷启动推荐问题，完全契合核心领域进展和LLM直接应用两个重点方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:44:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08181v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08181v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recommender systems (RS) are currently being studied to mitigate limitations during cold-start conditions by leveraging modality information or introducing Agent concepts based on the exceptional reasoning capabilities of Large Language Models (LLMs). Meanwhile, food and beverage recommender systems have traditionally used knowledge graph and ontology concepts due to the domain's unique data attributes and relationship characteristics. On this background, we propose MARC, a multimodal and multi-task cocktail recommender system based on Agentic Retrieval-Augmented Generation (RAG) utilizing graph database under cold-start conditions. The proposed system generates high-quality, contextually appropriate answers through two core processes: a task recognition router and a reflection process. The graph database was constructed by processing cocktail data from Kaggle, and its effectiveness was evaluated using 200 manually crafted questions. The evaluation used both LLM-as-a-judge and human evaluation to demonstrate that answers generated via the graph database outperformed those from a simple vector database in terms of quality. The code is available at https://github.com/diddbwls/cocktail_rec_agentrag
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08150v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DiffuGR：基于扩散语言模型的生成式文档检索
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DiffuGR: Generative Document Retrieval with Diffusion Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinpeng Zhao, Yukun Zhao, Zhenyang Li, Mengqi Zhang, Jun Feng, Ran Chen, Ying Zh...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究生成式文档检索中自回归方法的局限性，核心思想是将文档标识符生成建模为离散扩散过程，通过并行生成和可控去噪步骤实现更灵活的检索质量与延迟权衡。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对生成式检索的核心问题，提出基于扩散模型的非自回归生成方法，在检索效率和准确性权衡方面具有重要应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:00:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08150v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08150v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generative retrieval (GR) re-frames document retrieval as a sequence-based document identifier (DocID) generation task, memorizing documents with model parameters and enabling end-to-end retrieval without explicit indexing. Existing GR methods are based on auto-regressive generative models, i.e., the token generation is performed from left to right. However, such auto-regressive methods suffer from: (1) mismatch between DocID generation and natural language generation, e.g., an incorrect DocID token generated in early left steps would lead to totally erroneous retrieval; and (2) failure to balance the trade-off between retrieval efficiency and accuracy dynamically, which is crucial for practical applications. To address these limitations, we propose generative document retrieval with diffusion language models, dubbed DiffuGR. It models DocID generation as a discrete diffusion process: during training, DocIDs are corrupted through a stochastic masking process, and a diffusion language model is learned to recover them under a retrieval-aware objective. For inference, DiffuGR attempts to generate DocID tokens in parallel and refines them through a controllable number of denoising steps. In contrast to conventional left-to-right auto-regressive decoding, DiffuGR provides a novel mechanism to first generate more confident DocID tokens and refine the generation through diffusion-based denoising. Moreover, DiffuGR also offers explicit runtime control over the qualitylatency tradeoff. Extensive experiments on benchmark retrieval datasets show that DiffuGR is competitive with strong auto-regressive generative retrievers, while offering flexible speed and accuracy tradeoffs through variable denoising budgets. Overall, our results indicate that non-autoregressive diffusion models are a practical and effective alternative for generative document retrieval.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08006v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>从ID到语义：基于自适应语义分词技术的跨领域推荐生成式框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            From IDs to Semantics: A Generative Framework for Cross-Domain Recommendation with Adaptive Semantic Tokenization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peiyu Hu, Wayne Lu, Jia Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究跨域推荐中依赖共享ID的传统方法局限性问题，核心思想是通过领域自适应分词生成解耦的语义ID，并构建跨域自回归推荐框架来融合通用和领域特定兴趣。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对跨域推荐的核心挑战，提出基于语义ID生成的生成式框架，完美契合LLM在推荐系统的直接应用和Transformer架构创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:10:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08006v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08006v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Cross-domain recommendation (CDR) is crucial for improving recommendation accuracy and generalization, yet traditional methods are often hindered by the reliance on shared user/item IDs, which are unavailable in most real-world scenarios. Consequently, many efforts have focused on learning disentangled representations through multi-domain joint training to bridge the domain gaps. Recent Large Language Model (LLM)-based approaches show promise, they still face critical challenges, including: (1) the \textbf{item ID tokenization dilemma}, which leads to vocabulary explosion and fails to capture high-order collaborative knowledge; and (2) \textbf{insufficient domain-specific modeling} for the complex evolution of user interests and item semantics. To address these limitations, we propose \textbf{GenCDR}, a novel \textbf{Gen}erative \textbf{C}ross-\textbf{D}omain \textbf{R}ecommendation framework. GenCDR first employs a \textbf{Domain-adaptive Tokenization} module, which generates disentangled semantic IDs for items by dynamically routing between a universal encoder and domain-specific adapters. Symmetrically, a \textbf{Cross-domain Autoregressive Recommendation} module models user preferences by fusing universal and domain-specific interests. Finally, a \textbf{Domain-aware Prefix-tree} enables efficient and accurate generation. Extensive experiments on multiple real-world datasets demonstrate that GenCDR significantly outperforms state-of-the-art baselines. Our code is available in the supplementary materials.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08394v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>交互动态作为大语言模型的奖励信号
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Interaction Dynamics as a Reward Signal for LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sian Gooding, Edward Grefenstette
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多轮对话中LLM的对齐问题，核心思想是利用对话嵌入轨迹的几何特性（对话几何）作为奖励信号，证明交互动态本身与文本内容同样能有效预测对话成功。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的基于交互动态而非文本内容的奖励信号方法，直接适用于多轮对话推荐系统，为LLM在交互式应用中的对齐提供了新范式。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:11:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08394v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08394v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The alignment of Large Language Models (LLMs) for multi-turn conversations typically relies on reward signals derived from the content of the text. This approach, however, overlooks a rich, complementary source of signal: the dynamics of the interaction itself. This paper introduces TRACE (Trajectory-based Reward for Agent Collaboration Estimation), a novel reward signal derived from the geometric properties of a dialogue's embedding trajectory--a concept we term 'conversational geometry'. Our central finding is that a reward model trained only on these structural signals achieves a pairwise accuracy (68.20%) comparable to a powerful LLM baseline that analyzes the full transcript (70.04%). Furthermore, a hybrid model combining interaction dynamics with textual analysis achieves the highest performance (80.17%), demonstrating their complementary nature. This work provides strong evidence that for interactive settings, how an agent communicates is as powerful a predictor of success as what it says, offering a new, privacy-preserving framework that not only aligns agents but also serves as a diagnostic tool for understanding the distinct interaction patterns that drive successful collaboration.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07943v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Thinker：通过多轮交互训练大语言模型进行分层思考以实现深度搜索
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jun Xu, Xinkai Du, Yu Ao, Peilong Zhao, Yang Li, Ling Zhong, Lin Yuan, Zhongpu B...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何提升LLM在复杂搜索任务中的推理能力，核心思想是将复杂问题分解为可独立求解的子问题，并用自然语言和逻辑函数双重表示来支持知识库和网页搜索，同时通过逻辑函数传递子问题间依赖关系来增强逻辑连贯性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对搜索领域的核心挑战，提出层次化思考框架和多轮交互机制，通过逻辑函数表示和知识边界判定来增强LLM的推理过程，与搜索系统优化高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:48:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07943v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07943v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approaches neglect supervision over the reasoning process, making it difficult to guarantee logical coherence and rigor. To address these limitations, we propose Thinker, a hierarchical thinking model for deep search through multi-turn interaction, making the reasoning process supervisable and verifiable. It decomposes complex problems into independently solvable sub-problems, each dually represented in both natural language and an equivalent logical function to support knowledge base and web searches. Concurrently, dependencies between sub-problems are passed as parameters via these logical functions, enhancing the logical coherence of the problem-solving process. To avoid unnecessary external searches, we perform knowledge boundary determination to check if a sub-problem is within the LLM's intrinsic knowledge, allowing it to answer directly. Experimental results indicate that with as few as several hundred training samples, the performance of Thinker is competitive with established baselines. Furthermore, when scaled to the full training set, Thinker significantly outperforms these methods across various datasets and model sizes. The source code is available at https://github.com/OpenSPG/KAG-Thinker.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07969v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>统一工作嵌入：双向多任务排序器的对比学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Matthias De Lange, Jens-Joris Decorte, Jeroen Van Hautte
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何解决工作领域自然语言处理任务的长尾分布、极端多标签和稀疏数据问题；核心方法是构建任务特定二分图，通过多对多InfoNCE目标训练任务无关的双编码器，利用标记级嵌入和任务无关软延迟交互实现统一工作嵌入。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出统一多任务排序器和对比学习框架，直接适用于推荐系统核心架构；其任务无关嵌入和软延迟交互机制对大规模推荐系统有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:28:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07969v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07969v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Workforce transformation across diverse industries has driven an increased demand for specialized natural language processing capabilities. Nevertheless, tasks derived from work-related contexts inherently reflect real-world complexities, characterized by long-tailed distributions, extreme multi-label target spaces, and scarce data availability. The rise of generalist embedding models prompts the question of their performance in the work domain, especially as progress in the field has focused mainly on individual tasks. To this end, we introduce WorkBench, the first unified evaluation suite spanning six work-related tasks formulated explicitly as ranking problems, establishing a common ground for multi-task progress. Based on this benchmark, we find significant positive cross-task transfer, and use this insight to compose task-specific bipartite graphs from real-world data, synthetically enriched through grounding. This leads to Unified Work Embeddings (UWE), a task-agnostic bi-encoder that exploits our training-data structure with a many-to-many InfoNCE objective, and leverages token-level embeddings with task-agnostic soft late interaction. UWE demonstrates zero-shot ranking performance on unseen target spaces in the work domain, enables low-latency inference by caching the task target space embeddings, and shows significant gains in macro-averaged MAP and RP@10 over generalist embedding models.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08480v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>压缩后匹配：一种高效的多模态嵌入预训练范式
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Compression then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Ya...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多模态嵌入模型的预训练效率问题，核心思想是将语义理解与对比学习目标解耦，先通过压缩预训练阶段全面学习输入语义，再针对下游任务进行优化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出解耦预训练范式，将语义压缩与对比学习分离，这种高效训练架构对推荐系统中处理异构数据具有直接借鉴意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:23:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08480v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08480v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-language models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enhancing performance across a range of vision-language tasks, including cross-modal retrieval, clustering, and classification. An effective embedding is expected to comprehensively preserve the semantic content of the input while simultaneously emphasizing features that are discriminative for downstream tasks. Recent approaches demonstrate that VLMs can be adapted into competitive embedding models via large-scale contrastive learning, enabling the simultaneous optimization of two complementary objectives. We argue that the two aforementioned objectives can be decoupled: a comprehensive understanding of the input facilitates the embedding model in achieving superior performance in downstream tasks via contrastive learning. In this paper, we propose CoMa, a compressed pre-training phase, which serves as a warm-up stage for contrastive learning. Experiments demonstrate that with only a small amount of pre-training data, we can transform a VLM into a competitive embedding model. CoMa achieves new state-of-the-art results among VLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08319v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>对话系统中的自适应多智能体响应精炼
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Adaptive Multi-Agent Response Refinement in Conversational Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Soyeong Jeong, Aparna Elangovan, Emine Yilmaz, Oleg Rokhlenko
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何提升对话系统中LLM生成响应的质量，核心思想是采用多智能体框架，每个智能体专门负责事实性、个性化或连贯性等不同方面，并通过动态通信策略自适应协调智能体协作来优化响应。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出多智能体框架优化LLM响应，直接应用于对话系统，与个性化推荐和搜索高度相关。动态通信策略体现了Transformer架构的协作优化思想。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:48:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08319v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08319v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.MA</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have demonstrated remarkable success in conversational systems by generating human-like responses. However, they can fall short, especially when required to account for personalization or specific knowledge. In real-life settings, it is impractical to rely on users to detect these errors and request a new response. One way to address this problem is to refine the response before returning it to the user. While existing approaches focus on refining responses within a single LLM, this method struggles to consider diverse aspects needed for effective conversations. In this work, we propose refining responses through a multi-agent framework, where each agent is assigned a specific role for each aspect. We focus on three key aspects crucial to conversational quality: factuality, personalization, and coherence. Each agent is responsible for reviewing and refining one of these aspects, and their feedback is then merged to improve the overall response. To enhance collaboration among them, we introduce a dynamic communication strategy. Instead of following a fixed sequence of agents, our approach adaptively selects and coordinates the most relevant agents based on the specific requirements of each query. We validate our framework on challenging conversational datasets, demonstrating that ours significantly outperforms relevant baselines, particularly in tasks involving knowledge or user's persona, or both.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08274v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>多智能体图RAG：面向标签属性图的文本到Cypher框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anton Gusarov, Anastasia Volkova, Valentin Khrulkov, Andrey Kuznetsov, Evgenii M...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何利用结构化知识图谱数据增强检索增强生成系统，核心思想是开发一个多智能体LLM系统，将自然语言查询自动转换为Cypher查询语言，实现对标签属性图数据库的高效访问和推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了基于多智能体的GraphRAG框架，将文本转换为Cypher查询以访问标签属性图，直接应用LLM技术于结构化数据检索，与推荐和搜索系统中的知识图谱应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:04:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08274v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08274v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While Retrieval-Augmented Generation (RAG) methods commonly draw information from unstructured documents, the emerging paradigm of GraphRAG aims to leverage structured data such as knowledge graphs. Most existing GraphRAG efforts focus on Resource Description Framework (RDF) knowledge graphs, relying on triple representations and SPARQL queries. However, the potential of Cypher and Labeled Property Graph (LPG) databases to serve as scalable and effective reasoning engines within GraphRAG pipelines remains underexplored in current research literature. To fill this gap, we propose Multi-Agent GraphRAG, a modular LLM agentic system for text-to-Cypher query generation serving as a natural language interface to LPG-based graph data. Our proof-of-concept system features an LLM-based workflow for automated Cypher queries generation and execution, using Memgraph as the graph database backend. Iterative content-aware correction and normalization, reinforced by an aggregated feedback loop, ensures both semantic and syntactic refinement of generated queries. We evaluate our system on the CypherBench graph dataset covering several general domains with diverse types of queries. In addition, we demonstrate performance of the proposed workflow on a property graph derived from the IFC (Industry Foundation Classes) data, representing a digital twin of a building. This highlights how such an approach can bridge AI with real-world applications at scale, enabling industrial digital automation use cases.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08505v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>用于回答聚合问题的结构化检索增强生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Structured RAG for Answering Aggregative Questions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Omri Koshorek, Niv Granot, Aviv Alloni, Shahar Admati, Roee Hendel, Ido Weiss, A...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何改进RAG系统以处理需要从大量文档中聚合信息的复杂查询问题，核心方法是构建语料库的结构化表示并将自然语言查询转换为对该结构的正式查询。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文针对聚合查询的RAG系统改进，直接关联搜索领域的核心问题，提出的结构化表示方法对复杂信息检索有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:39:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08505v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08505v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) has become the dominant approach for answering questions over large corpora. However, current datasets and methods are highly focused on cases where only a small part of the corpus (usually a few paragraphs) is relevant per query, and fail to capture the rich world of aggregative queries. These require gathering information from a large set of documents and reasoning over them. To address this gap, we propose S-RAG, an approach specifically designed for such queries. At ingestion time, S-RAG constructs a structured representation of the corpus; at inference time, it translates natural-language queries into formal queries over said representation. To validate our approach and promote further research in this area, we introduce two new datasets of aggregative queries: HOTELS and WORLD CUP. Experiments with S-RAG on the newly introduced datasets, as well as on a public benchmark, demonstrate that it substantially outperforms both common RAG systems and long-context LLMs.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08043v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DynaAct：基于动态动作空间的大语言模型推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DynaAct: Large Language Model Reasoning with Dynamic Action Spaces
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xueliang Zhao, Wei Wu, Jian Guan, Qintong Li, Lingpeng Kong
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究复杂问题求解中序列推理的效率问题，核心方法是利用大语言模型从多样推理问题中提取通用草图作为代理动作空间，然后通过子模函数联合评估候选动作的效用和多样性来动态构建最优候选集。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出动态构建紧凑动作空间的方法，直接适用于推荐和搜索系统中的候选集生成问题，其核心思想与序列决策系统高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:47:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08043v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08043v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In modern sequential decision-making systems, the construction of an optimal candidate action space is critical to efficient inference. However, existing approaches either rely on manually defined action spaces that lack scalability or utilize unstructured spaces that render exhaustive search computationally prohibitive. In this paper, we propose a novel framework named \textsc{DynaAct} for automatically constructing a compact action space to enhance sequential reasoning in complex problem-solving scenarios. Our method first estimates a proxy for the complete action space by extracting general sketches observed in a corpus covering diverse complex reasoning problems using large language models. We then formulate a submodular function that jointly evaluates candidate actions based on their utility to the current state and their diversity, and employ a greedy algorithm to select an optimal candidate set. Extensive experiments on six diverse standard benchmarks demonstrate that our approach significantly improves overall performance, while maintaining efficient inference without introducing substantial latency. The implementation is available at https://github.com/zhaoxlpku/DynaAct.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07896v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>SparseRM：基于稀疏自编码器的轻量级偏好建模
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SparseRM: A Lightweight Preference Modeling with Sparse Autoencoder
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dengcan Liu, Jiahao Li, Zheren Fu, Yi Tu, Jiajun Li, Zhendong Mao, Yongdong Zhan...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究在有限资源下训练可靠奖励模型的核心问题，核心方法是利用稀疏自编码器从LLM表示中提取偏好相关特征，构建轻量级可解释的奖励模型。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出使用稀疏自编码器构建轻量级奖励模型，直接应用于LLM对齐流程，与偏好建模和高效Transformer技术高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:51:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07896v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07896v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reward models (RMs) are a core component in the post-training of large language models (LLMs), serving as proxies for human preference evaluation and guiding model alignment. However, training reliable RMs under limited resources remains challenging due to the reliance on large-scale preference annotations and the high cost of fine-tuning LLMs. To address this, we propose SparseRM, which leverages Sparse Autoencoder (SAE) to extract preference-relevant information encoded in model representations, enabling the construction of a lightweight and interpretable reward model. SparseRM first employs SAE to decompose LLM representations into interpretable directions that capture preference-relevant features. The representations are then projected onto these directions to compute alignment scores, which quantify the strength of each preference feature in the representations. A simple reward head aggregates these scores to predict preference scores. Experiments on three preference modeling tasks show that SparseRM achieves superior performance over most mainstream RMs while using less than 1% of trainable parameters. Moreover, it integrates seamlessly into downstream alignment pipelines, highlighting its potential for efficient alignment.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07800v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>从经验到策略：通过可训练图记忆赋能大语言模型智能体
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siyu Xia, Zekun Xu, Jiajun Chai, Wentian Fan, Yan Song, Xiaohan Wang, Guojun Yin...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何提升LLM智能体利用先验经验指导决策的能力，核心方法是构建可训练的多层图记忆框架，将原始轨迹抽象为结构化决策路径并提炼为高层战略元认知，通过强化学习优化策略效用并动态集成到训练循环中。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的可训练图记忆框架直接增强LLM智能体的推理能力，其强化学习优化和元认知提示方法在搜索和推荐系统中具有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:36:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07800v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07800v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving across complex, open-ended environments. A promising approach for improving the reasoning capabilities of LLM agents is to better utilize prior experiences in guiding current decisions. However, LLMs acquire experience either through implicit memory via training, which suffers from catastrophic forgetting and limited interpretability, or explicit memory via prompting, which lacks adaptability. In this paper, we introduce a novel agent-centric, trainable, multi-layered graph memory framework and evaluate how context memory enhances the ability of LLMs to utilize parametric information. The graph abstracts raw agent trajectories into structured decision paths in a state machine and further distills them into high-level, human-interpretable strategic meta-cognition. In order to make memory adaptable, we propose a reinforcement-based weight optimization procedure that estimates the empirical utility of each meta-cognition based on reward feedback from downstream tasks. These optimized strategies are then dynamically integrated into the LLM agent's training loop through meta-cognitive prompting. Empirically, the learnable graph memory delivers robust generalization, improves LLM agents' strategic reasoning performance, and provides consistent benefits during Reinforcement Learning (RL) training.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08579v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>训练语言模型解释其自身计算过程
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Training Language Models to Explain Their Own Computations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Belinda Z. Li, Zifan Carl Guo, Vincent Huang, Jacob Steinhardt, Jacob Andreas
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究语言模型能否学习描述自身内部计算的问题，核心思想是利用语言模型对其内部状态的独特访问权限，通过微调让模型生成关于特征编码、激活因果结构和输入影响的语言描述，作为现有可解释性方法的补充。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究语言模型自我解释内部计算的方法，直接关联LLM可解释性技术，对推荐系统和搜索领域的模型透明度与可信度有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:57:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08579v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08579v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs' privileged access to their own internals can be leveraged to produce new techniques for explaining their behavior. Using existing interpretability techniques as a source of ground truth, we fine-tune LMs to generate natural language descriptions of (1) the information encoded by LM features, (2) the causal structure of LMs' internal activations, and (3) the influence of specific input tokens on LM outputs. When trained with only tens of thousands of example explanations, explainer models exhibit non-trivial generalization to new queries. This generalization appears partly attributable to explainer models' privileged access to their own internals: using a model to explain its own computations generally works better than using a *different* model to explain its computations (even if the other model is significantly more capable). Our results suggest not only that LMs can learn to reliably explain their internal computations, but that such explanations offer a scalable complement to existing interpretability methods.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08368v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>循环论证：RoPE在视觉领域是否需要保持等变性？
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            A Circular Argument : Does RoPE need to be Equivariant for Vision?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chase van de Geijn, Timo Lüddecke, Polina Turishcheva, Alexander S. Ecker
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究旋转位置编码(RoPE)在视觉任务中的等变性要求问题，核心发现是打破严格等变性的非交换生成器方法在视觉任务中表现更优，挑战了相对位置编码必须保持等变性的传统认知。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文深入探讨Transformer位置编码机制，直接关联Transformer架构效率与注意力机制优化，对搜索推荐系统中的序列建模具有重要启示。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:47:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08368v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08368v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Rotary Positional Encodings (RoPE) have emerged as a highly effective technique for one-dimensional sequences in Natural Language Processing spurring recent progress towards generalizing RoPE to higher-dimensional data such as images and videos. The success of RoPE has been thought to be due to its positional equivariance, i.e. its status as a relative positional encoding. In this paper, we mathematically show RoPE to be one of the most general solutions for equivariant positional embedding in one-dimensional data. Moreover, we show Mixed RoPE to be the analogously general solution for M-dimensional data, if we require commutative generators -- a property necessary for RoPE's equivariance. However, we question whether strict equivariance plays a large role in RoPE's performance. We propose Spherical RoPE, a method analogous to Mixed RoPE, but assumes non-commutative generators. Empirically, we find Spherical RoPE to have the equivalent or better learning behavior compared to its equivariant analogues. This suggests that relative positional embeddings are not as important as is commonly believed, at least within computer vision. We expect this discovery to facilitate future work in positional encodings for vision that can be faster and generalize better by removing the preconception that they must be relative.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08066v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>信息容量：通过文本压缩评估大型语言模型的效率
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cheng Yuan, Jiawei Shao, Chi Zhang, Xuelong Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何统一评估不同规模和架构的LLM效率问题，核心思想是通过文本压缩性能与计算复杂度的比值定义信息容量指标，该指标能公平比较模型系列间的效率并准确预测系列内性能。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出信息容量作为LLM效率的统一评估指标，直接关联模型压缩能力与计算复杂度，对推荐和搜索系统的模型选择与效率优化具有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:07:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08066v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08066v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">eess.SP</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent years have witnessed the rapid advancements of large language models (LLMs) and their expanding applications, leading to soaring demands for computational resources. The widespread adoption of test-time scaling further aggravates the tension between model capability and resource consumption, highlighting the importance of inference efficiency. However, a unified metric that accurately reflects an LLM's efficiency across different model sizes and architectures remains absent. Motivated by the correlation between compression and intelligence, we introduce information capacity, a measure of model efficiency based on text compression performance relative to computational complexity. Larger models can predict the next token more accurately, achieving greater compression gains but at higher computational costs. Empirical evaluations on mainstream open-source models show that models of varying sizes within a series exhibit consistent information capacity. This metric enables a fair efficiency comparison across model series and accurate performance prediction within a model series. A distinctive feature of information capacity is that it incorporates tokenizer efficiency, which affects both input and output token counts but is often neglected in LLM evaluations. We assess the information capacity of 49 models on 5 heterogeneous datasets and observe consistent results on the influences of tokenizer efficiency, pretraining data, and the mixture-of-experts architecture.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08577v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Think-at-Hard：选择性潜在迭代以改进推理语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianyu Fu, Yichen You, Zekai Chen, Guohao Dai, Huazhong Yang, Yu Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何提升语言模型的推理能力，核心思想是仅对预测困难的token进行深度迭代优化，通过轻量级决策器动态选择需要优化的token，并利用LoRA模块将模型目标从通用预测切换到困难token精调。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的动态迭代机制和注意力扩展方法对推荐系统处理困难样本具有直接借鉴意义，LoRA模块的目标切换策略可应用于个性化推荐场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:57:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08577v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08577v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.PF</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Improving reasoning capabilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Prior work proposes recurrent transformers, which allocate a fixed number of extra iterations per token to improve generation quality. After the first, standard forward pass, instead of verbalization, last-layer hidden states are fed back as inputs for additional iterations to refine token predictions. Yet we identify a latent overthinking phenomenon: easy token predictions that are already correct after the first pass are sometimes revised into errors in additional iterations. To address this, we propose Think-at-Hard (TaH), a dynamic latent thinking method that iterates deeper only at hard tokens. It employs a lightweight neural decider to trigger latent iterations only at tokens that are likely incorrect after the standard forward pass. During latent iterations, Low-Rank Adaptation (LoRA) modules shift the LLM objective from general next-token prediction to focused hard-token refinement. We further introduce a duo-causal attention mechanism that extends attention from the token sequence dimension to an additional iteration depth dimension. This enables cross-iteration information flow while maintaining full sequential parallelism. Experiments show that TaH boosts LLM reasoning performance across five challenging benchmarks while maintaining the same parameter count. Compared with baselines that iterate twice for all output tokens, TaH delivers 8.1-11.3% accuracy gains while exempting 94% of tokens from the second iteration. Against strong single-iteration Qwen3 models finetuned with the same data, it also delivers 4.0-5.0% accuracy gains. When allowing less than 3% additional parameters from LoRA and the iteration decider, the gains increase to 8.5-12.6% and 5.3-5.4%, respectively. Our code is available at https://github.com/thu-nics/TaH.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08128v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向长上下文大语言模型的句子锚定要点压缩
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Sentence-Anchored Gist Compression for Long-Context LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dmitrii Tarasov, Elizaveta Goncharova, Kuznetsov Andrey
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何降低长序列处理的计算和内存需求，核心方法是微调预训练LLM使用学习到的压缩标记来压缩上下文，实现2-8倍的压缩率。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究长上下文压缩技术，直接关联LLM效率提升，对搜索和推荐系统中的长序列处理有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:34:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08128v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08128v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This work investigates context compression for Large Language Models (LLMs) using learned compression tokens to reduce the memory and computational demands of processing long sequences. We demonstrate that pre-trained LLMs can be fine-tuned to compress their context by factors of 2x to 8x without significant performance degradation, as evaluated on both short-context and long-context benchmarks. Furthermore, in experiments on a 3-billion-parameter LLaMA model, our method achieves results on par with alternative compression techniques while attaining higher compression ratios.
                </div>
            </details>
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07910v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>最后一层逻辑到逻辑：赋能大语言模型进行逻辑一致的结构化知识推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Songze Li, Zhiqiang Liu, Zhaoyan Gong, Xiaoke Guo, Zhengke Gui, Huajun Chen, Wen...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM在结构化知识推理中的逻辑漂移问题，核心思想是通过对数强化和对数过滤模块在自回归生成过程中修正LLM输出的逻辑缺陷，提升逻辑一致性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的Logits-to-Logic框架直接针对LLM输出层的逻辑缺陷进行修正，属于核心LLM技术进展，对搜索和推荐系统中的知识推理任务具有直接应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:08:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07910v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07910v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabling them to understand the logic in natural language and generate logic-consistent responses. However, the representational differences between unstructured and structured knowledge make LLMs inherently struggle to maintain logic consistency, leading to \textit{Logic Drift} challenges in structured knowledge reasoning tasks such as Knowledge Graph Question Answering (KGQA). Existing methods address this limitation by designing complex workflows embedded in prompts to guide LLM reasoning. Nevertheless, these approaches only provide input-level guidance and fail to fundamentally address the \textit{Logic Drift} in LLM outputs. Additionally, their inflexible reasoning workflows cannot adapt to different tasks and knowledge graphs. To enhance LLMs' logic consistency in structured knowledge reasoning, we specifically target the logits output from the autoregressive generation process. We propose the \textit{Logits-to-Logic} framework, which incorporates logits strengthening and logits filtering as core modules to correct logical defects in LLM outputs. Extensive experiments show that our approach significantly improves LLMs' logic consistency in structured knowledge reasoning and achieves state-of-the-art performance on multiple KGQA benchmarks.
                </div>
            </details>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08238v1" target="_blank" rel="noopener noreferrer">
                视觉-语言微调中语义关系的重塑
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Remodeling Semantic Relationships in Vision-Language Fine-Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiangyang Wu, Liu Liu, Baosheng Yu, Jiayan Qiu, Zhenwei Shi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">This paper addresses semantic relationship remodeling in vision-language fine-tuning, which directly relates to the VLM analogy for heterogeneous data focus area. The techniques for modeling relationships between different modalities (vision and language) could be directly applied to modeling heterogeneous user data and context features in recommendation systems, treating them as distinct modalities similar to how VLMs handle vision and language.</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:37:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08238v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08238v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-language fine-tuning has emerged as an efficient paradigm for constructing multimodal foundation models. While textual context often highlights semantic relationships within an image, existing fine-tuning methods typically overlook this information when aligning vision and language, thus leading to suboptimal performance. Toward solving this problem, we propose a method that can improve multimodal alignment and fusion based on both semantics and relationships.Specifically, we first extract multilevel semantic features from different vision encoder to capture more visual cues of the relationships. Then, we learn to project the vision features to group related semantics, among which are more likely to have relationships. Finally, we fuse the visual features with the textual by using inheritable cross-attention, where we globally remove the redundant visual relationships by discarding visual-language feature pairs with low correlation. We evaluate our proposed method on eight foundation models and two downstream tasks, visual question answering and image captioning, and show that it outperforms all existing methods.
                </div>
            </details>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08399v1" target="_blank" rel="noopener noreferrer">
                通过错位实现对齐：面向多模态对齐的边界感知课程学习
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hua Ye, Hang Ding, Siyuan Chen, Yiyang Jiang, Changyuan Zhang, Xuan Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种边界感知的课程学习方法用于多模态对齐，这属于'VLM Analogy for Heterogeneous Data'范畴。多模态对齐技术可以应用于推荐系统和搜索中处理异构数据（如用户行为序列、上下文特征、内容特征等），通过更好的模态对齐提升跨模态检索和推荐性能。课程学习策略可以优化模型训练过程，提高对齐质量。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:15:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08399v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08399v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum with Local Attention (BACL), a lightweight add-on that turns these borderline cases into a curriculum signal. A Boundary-aware Negative Sampler gradually raises difficulty, while a Contrastive Local Attention loss highlights where the mismatch occurs. The two modules are fully differentiable and work with any off-the-shelf dual encoder. Theory predicts a fast O(1/n) error rate; practice shows up to +32% R@1 over CLIP and new SOTA on four large-scale benchmarks, all without extra labels.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07710v1" target="_blank" rel="noopener noreferrer">
                通过粒度感知和区域不确定性建模实现跨模态细粒度对齐
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
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        <div class="mb-2 text-base text-gray-700">
            Cross Modal Fine-grained Alignment via Granularity-aware and Region-uncertain Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiale Liu, Haoming Zhou, Yishu Zhu, Bingzhi Chen, Yuncheng Jiang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的跨模态细粒度对齐技术直接类比于VLM处理异构数据的思路，可用于推荐系统中用户行为序列与商品特征的跨模态对齐。粒度感知和不确定性建模方法可应用于搜索和广告中的多模态特征融合，提升异构数据联合建模的效果。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 00:28:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07710v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07710v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Fine-grained image-text alignment is a pivotal challenge in multimodal learning, underpinning key applications such as visual question answering, image captioning, and vision-language navigation. Unlike global alignment, fine-grained alignment requires precise correspondence between localized visual regions and textual tokens, often hindered by noisy attention mechanisms and oversimplified modeling of cross-modal relationships. In this work, we identify two fundamental limitations of existing approaches: the lack of robust intra-modal mechanisms to assess the significance of visual and textual tokens, leading to poor generalization in complex scenes; and the absence of fine-grained uncertainty modeling, which fails to capture the one-to-many and many-to-one nature of region-word correspondences. To address these issues, we propose a unified approach that incorporates significance-aware and granularity-aware modeling and region-level uncertainty modeling. Our method leverages modality-specific biases to identify salient features without relying on brittle cross-modal attention, and represents region features as a mixture of Gaussian distributions to capture fine-grained uncertainty. Extensive experiments on Flickr30K and MS-COCO demonstrate that our approach achieves state-of-the-art performance across various backbone architectures, significantly enhancing the robustness and interpretability of fine-grained image-text alignment.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08298v1" target="_blank" rel="noopener noreferrer">
                基于视觉大语言模型的复杂表格层次结构理解：基准与实验
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Hierarchical structure understanding in complex tables with VLLMs: a benchmark and experiments
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luca Bindini, Simone Giovannini, Simone Marinai, Valeria Nardoni, Kimiya Noor Al...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及视觉语言模型在复杂表格理解中的应用，这与'VLM类比异构数据'焦点相关，因为表格可以视为包含结构化数据和文本的异构模态。在搜索和推荐系统中，表格理解技术可以用于处理产品规格、价格比较等结构化信息，提升信息检索和内容理解能力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:32:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08298v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08298v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This work investigates the ability of Vision Large Language Models (VLLMs) to understand and interpret the structure of tables in scientific articles. Specifically, we explore whether VLLMs can infer the hierarchical structure of tables without additional processing. As a basis for our experiments we use the PubTables-1M dataset, a large-scale corpus of scientific tables. From this dataset, we extract a subset of tables that we introduce as Complex Hierarchical Tables (CHiTab): a benchmark collection of complex tables containing hierarchical headings. We adopt a series of prompt engineering strategies to probe the models' comprehension capabilities, experimenting with various prompt formats and writing styles. Multiple state-of-the-art open-weights VLLMs are evaluated on the benchmark first using their off-the-shelf versions and then fine-tuning some models on our task. We also measure the performance of humans to solve the task on a small set of tables comparing with performance of the evaluated VLLMs. The experiments support our intuition that generic VLLMs, not explicitly designed for understanding the structure of tables, can perform this task. This study provides insights into the potential and limitations of VLLMs to process complex tables and offers guidance for future work on integrating structured data understanding into general-purpose VLLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.08113v1" target="_blank" rel="noopener noreferrer">
                多模态大语言模型在跨模态技能组合方面未达到最优
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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        <div class="mb-2 text-base text-gray-700">
            Multimodal LLMs Do Not Compose Skills Optimally Across Modalities
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态LLM的跨模态组合能力，这属于'Enabling LLM Tech'范畴，对搜索和推荐系统有潜在应用价值。多模态理解能力可以增强搜索引擎处理图像-文本查询的能力，或改进推荐系统对异构内容（如商品图片与描述）的统一建模。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:11:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08113v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08113v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Skill composition is the ability to combine previously learned skills to solve new tasks. As neural networks acquire increasingly complex skills during their pretraining, it is not clear how successfully they can compose them. In this paper, we focus on Multimodal Large Language Models (MLLM), and study their ability to compose skills across modalities. To this end, we design three evaluation tasks which can be solved sequentially composing two modality-dependent skills, and evaluate several open MLLMs under two main settings: i) prompting the model to directly solve the task, and ii) using a two-step cascaded inference approach, which manually enforces the composition of the two skills for a given task. Even with these straightforward compositions, we find that all evaluated MLLMs exhibit a significant cross-modality skill composition gap. To mitigate the aforementioned gap, we explore two alternatives: i) use chain-of-thought prompting to explicitly instruct MLLMs for skill composition and ii) a specific fine-tuning recipe to promote skill composition. Although those strategies improve model performance, they still exhibit significant skill composition gaps, suggesting that more research is needed to improve cross-modal skill composition in MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.08544v1" target="_blank" rel="noopener noreferrer">
                LeJEPA：无需启发式方法的可证明且可扩展的自监督学习
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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            LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Randall Balestriero, Yann LeCun
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种新的自监督学习方法，专注于可证明性和可扩展性。自监督学习是LLM预训练的核心技术，这种基础性进步可以显著提升推荐系统和搜索中使用的语言模型的质量和效率。虽然不直接应用于特定领域，但作为使能技术具有重要价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:21:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08544v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08544v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">stat.ML</span></div>
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                    Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of practical guidance and theory has led to ad-hoc R&D. We present a comprehensive theory of JEPAs and instantiate it in {\bf LeJEPA}, a lean, scalable, and theoretically grounded training objective. First, we identify the isotropic Gaussian as the optimal distribution that JEPAs' embeddings should follow to minimize downstream prediction risk. Second, we introduce a novel objective--{\bf Sketched Isotropic Gaussian Regularization} (SIGReg)--to constrain embeddings to reach that ideal distribution. Combining the JEPA predictive loss with SIGReg yields LeJEPA with numerous theoretical and practical benefits: (i) single trade-off hyperparameter, (ii) linear time and memory complexity, (iii) stability across hyper-parameters, architectures (ResNets, ViTs, ConvNets) and domains, (iv) heuristics-free, e.g., no stop-gradient, no teacher-student, no hyper-parameter schedulers, and (v) distributed training-friendly implementation requiring only $\approx$50 lines of code. Our empirical validation covers 10+ datasets, 60+ architectures, all with varying scales and domains. As an example, using imagenet-1k for pretraining and linear evaluation with frozen backbone, LeJEPA reaches 79\% with a ViT-H/14. We hope that the simplicity and theory-friendly ecosystem offered by LeJEPA will reestablish self-supervised pre-training as a core pillar of AI research (\href{git@github.com:rbalestr-lab/lejepa.git}{GitHub repo}).
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            <a href="https://www.alphaxiv.org/abs/2511.08500v1" target="_blank" rel="noopener noreferrer">
                SPEAR-MM：通过模型合并实现选择性参数评估与恢复的高效金融大语言模型适配
            </a>
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        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>4/10
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        <div class="mb-2 text-base text-gray-700">
            SPEAR-MM: Selective Parameter Evaluation and Restoration via Model Merging for Efficient Financial LLM Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Berkcan Kapusuzoglu, Supriyo Chakraborty, Renkun Ni, Stephen Rawls, Sambit Sahu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注金融领域LLM的高效适配技术，虽然涉及模型合并和参数效率优化，但核心应用领域是金融而非搜索/推荐/广告。选择性参数评估与恢复技术可能对推荐系统的模型微调有一定启发，但这种关联性较弱且间接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:34:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08500v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08500v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">math.SP</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) adapted to financial domains often suffer from catastrophic forgetting of general reasoning capabilities essential for customer interactions and complex financial analysis. We introduce Selective Parameter Evaluation and Restoration via Model Merging (SPEAR-MM), a practical framework that preserves critical capabilities while enabling domain adaptation. Our method approximates layer-wise impact on external benchmarks through post-hoc analysis, then selectively freezes or restores transformer layers via spherical interpolation merging. Applied to LLaMA-3.1-8B for financial tasks, SPEAR-MM achieves 91.2% retention of general capabilities versus 69.7% for standard continual pretraining, while maintaining 94% of domain adaptation gains. The approach provides interpretable trade-off control and reduces computational costs by 90% crucial for resource-constrained financial institutions.
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            <a href="https://www.alphaxiv.org/abs/2511.08476v1" target="_blank" rel="noopener noreferrer">
                通过新型机器可读知识数字图书馆推进科学知识检索与重用
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Advancing Scientific Knowledge Retrieval and Reuse with a Novel Digital Library for Machine-Readable Knowledge
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hadi Ghaemi, Lauren Snyder, Markus Stocker
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注科学知识检索的数字图书馆建设，虽然涉及检索系统，但更偏向特定领域（科学知识）而非通用的搜索或推荐系统。其机器可读知识的概念可能对异构数据处理有轻微启发，但缺乏明确的Transformer或LLM技术连接，且科学领域应用超出当前关注范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:20:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08476v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08476v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Digital libraries for research, such as the ACM Digital Library or Semantic Scholar, do not enable the machine-supported, efficient reuse of scientific knowledge (e.g., in synthesis research). This is because these libraries are based on document-centric models with narrative text knowledge expressions that require manual or semi-automated knowledge extraction, structuring, and organization. We present ORKG reborn, an emerging digital library that supports finding, accessing, and reusing accurate, fine-grained, and reproducible machine-readable expressions of scientific knowledge that relate scientific statements and their supporting evidence in terms of data and code. The rich expressions of scientific knowledge are published as reborn (born-reusable) articles and provide novel possibilities for scientific knowledge retrieval, for instance by statistical methods, software packages, variables, or data matching specific constraints. We describe the proposed system and demonstrate its practical viability and potential for information retrieval in contrast to state-of-the-art digital libraries and document-centric scholarly communication using several published articles in research fields ranging from computer science to soil science. Our work underscores the enormous potential of scientific knowledge databases and a viable approach to their construction.
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            <a href="https://www.alphaxiv.org/abs/2511.08325v1" target="_blank" rel="noopener noreferrer">
                AgentPRM：通过逐步承诺与进展为LLM智能体构建过程奖励模型
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            AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiheng Xi, Chenyang Liao, Guanyu Li, Yajie Yang, Wenxiang Chen, Zhihao Zhang, B...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM智能体的过程奖励建模，属于强化学习技术范畴。虽然奖励模型在推荐系统中可能有潜在应用（如用户交互优化），但论文标题明确聚焦于智能体过程控制，与RecSys/Search/Ads的核心排名和检索任务关联度较低，且未明确展示在推荐领域的直接应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:57:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08325v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08325v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite rapid development, large language models (LLMs) still encounter challenges in multi-turn decision-making tasks (i.e., agent tasks) like web shopping and browser navigation, which require making a sequence of intelligent decisions based on environmental feedback. Previous work for LLM agents typically relies on elaborate prompt engineering or fine-tuning with expert trajectories to improve performance. In this work, we take a different perspective: we explore constructing process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process. Unlike LLM reasoning, where each step is scored based on correctness, actions in agent tasks do not have a clear-cut correctness. Instead, they should be evaluated based on their proximity to the goal and the progress they have made. Building on this insight, we propose a re-defined PRM for agent tasks, named AgentPRM, to capture both the interdependence between sequential decisions and their contribution to the final goal. This enables better progress tracking and exploration-exploitation balance. To scalably obtain labeled data for training AgentPRM, we employ a Temporal Difference-based (TD-based) estimation method combined with Generalized Advantage Estimation (GAE), which proves more sample-efficient than prior methods. Extensive experiments across different agentic tasks show that AgentPRM is over $8\times$ more compute-efficient than baselines, and it demonstrates robust improvement when scaling up test-time compute. Moreover, we perform detailed analyses to show how our method works and offer more insights, e.g., applying AgentPRM to the reinforcement learning of LLM agents.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08525v1" target="_blank" rel="noopener noreferrer">
                探究大型推理模型中的思维链可监控性
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Investigating CoT Monitorability in Large Reasoning Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shu Yang, Junchao Wu, Xilin Gou, Xuansheng Wu, Derek Wong, Ninhao Liu, Di Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注思维链(CoT)的监控性，这属于推理过程的可解释性范畴。虽然思维链技术可用于增强推荐和搜索系统的可解释性，但论文主要聚焦于监控机制本身而非直接应用于推荐/搜索/广告领域。作为一项基础性研究，其潜在应用路径不够明确直接。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:06:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08525v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08525v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks by engaging in extended reasoning before producing final answers. Beyond improving abilities, these detailed reasoning traces also create a new opportunity for AI safety, CoT Monitorability: monitoring potential model misbehavior, such as the use of shortcuts or sycophancy, through their chain-of-thought (CoT) during decision-making. However, two key fundamental challenges arise when attempting to build more effective monitors through CoT analysis. First, as prior research on CoT faithfulness has pointed out, models do not always truthfully represent their internal decision-making in the generated reasoning. Second, monitors themselves may be either overly sensitive or insufficiently sensitive, and can potentially be deceived by models' long, elaborate reasoning traces. In this paper, we present the first systematic investigation of the challenges and potential of CoT monitorability. Motivated by two fundamental challenges we mentioned before, we structure our study around two central perspectives: (i) verbalization: to what extent do LRMs faithfully verbalize the true factors guiding their decisions in the CoT, and (ii) monitor reliability: to what extent can misbehavior be reliably detected by a CoT-based monitor? Specifically, we provide empirical evidence and correlation analyses between verbalization quality, monitor reliability, and LLM performance across mathematical, scientific, and ethical domains. Then we further investigate how different CoT intervention methods, designed to improve reasoning efficiency or performance, will affect monitoring effectiveness. Finally, we propose MoME, a new paradigm in which LLMs monitor other models' misbehavior through their CoT and provide structured judgments along with supporting evidence.
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            <a href="https://www.alphaxiv.org/abs/2511.08098v1" target="_blank" rel="noopener noreferrer">
                PerspAct：通过视角采择与主动视觉增强大语言模型的情境协作能力
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            PerspAct: Enhancing LLM Situated Collaboration Skills through Perspective Taking and Active Vision
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sabrina Patania, Luca Annese, Anita Pellegrini, Silvia Serino, Anna Lambiase, Lu...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在具体情境中的协作能力提升，属于LLM能力增强范畴，但未明确涉及推荐系统、搜索或广告领域的应用。虽然视角采择和主动视觉技术可能对理解用户意图有帮助，但论文标题未展示这些技术如何直接应用于RecSys/Search/Ads场景，因此相关性有限。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:54:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08098v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in Large Language Models (LLMs) and multimodal foundation models have significantly broadened their application in robotics and collaborative systems. However, effective multi-agent interaction necessitates robust perspective-taking capabilities, enabling models to interpret both physical and epistemic viewpoints. Current training paradigms often neglect these interactive contexts, resulting in challenges when models must reason about the subjectivity of individual perspectives or navigate environments with multiple observers. This study evaluates whether explicitly incorporating diverse points of view using the ReAct framework, an approach that integrates reasoning and acting, can enhance an LLM's ability to understand and ground the demands of other agents. We extend the classic Director task by introducing active visual exploration across a suite of seven scenarios of increasing perspective-taking complexity. These scenarios are designed to challenge the agent's capacity to resolve referential ambiguity based on visual access and interaction, under varying state representations and prompting strategies, including ReAct-style reasoning. Our results demonstrate that explicit perspective cues, combined with active exploration strategies, significantly improve the model's interpretative accuracy and collaborative effectiveness. These findings highlight the potential of integrating active perception with perspective-taking mechanisms in advancing LLMs' application in robotics and multi-agent systems, setting a foundation for future research into adaptive and context-aware AI systems.
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            <a href="https://www.alphaxiv.org/abs/2511.07772v1" target="_blank" rel="noopener noreferrer">
                SALT：在思维链中引导激活向量实现无泄漏思考
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            SALT: Steering Activations towards Leakage-free Thinking in Chain of Thought
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shourya Batra, Pierce Tillman, Samarth Gaggar, Shashank Kesineni, Kevin Zhu, Sun...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注思维链推理中的激活引导技术，属于LLM推理优化范畴。虽然思维链技术本身在搜索和推荐中有应用潜力（如解释生成），但论文聚焦于防止信息泄漏的特定机制，与推荐/搜索系统的核心架构进步关联较弱。这种推理优化技术可能间接提升LLM在搜索问答中的可靠性，但直接应用价值有限。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 02:45:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07772v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07772v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As Large Language Models (LLMs) evolve into personal assistants with access to sensitive user data, they face a critical privacy challenge: while prior work has addressed output-level privacy, recent findings reveal that LLMs often leak private information through their internal reasoning processes, violating contextual privacy expectations. These leaky thoughts occur when models inadvertently expose sensitive details in their reasoning traces, even when final outputs appear safe. The challenge lies in preventing such leakage without compromising the model's reasoning capabilities, requiring a delicate balance between privacy and utility. We introduce Steering Activations towards Leakage-free Thinking (SALT), a lightweight test-time intervention that mitigates privacy leakage in model's Chain of Thought (CoT) by injecting targeted steering vectors into hidden state. We identify the high-leakage layers responsible for this behavior. Through experiments across multiple LLMs, we demonstrate that SALT achieves reductions including $18.2\%$ reduction in CPL on QwQ-32B, $17.9\%$ reduction in CPL on Llama-3.1-8B, and $31.2\%$ reduction in CPL on Deepseek in contextual privacy leakage dataset AirGapAgent-R while maintaining comparable task performance and utility. Our work establishes SALT as a practical approach for test-time privacy protection in reasoning-capable language models, offering a path toward safer deployment of LLM-based personal agents.
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            <a href="https://www.alphaxiv.org/abs/2511.08522v1" target="_blank" rel="noopener noreferrer">
                AlphaResearch：利用语言模型加速新算法发现
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            AlphaResearch: Accelerating New Algorithm Discovery with Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhaojian Yu, Kaiyue Feng, Yilun Zhao, Shilin He, Xiao-Ping Zhang, Arman Cohan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用语言模型进行算法发现，这属于LLM的通用应用领域，而非专门针对推荐系统、搜索或广告。虽然LLM技术本身是使能技术，但论文标题未表明其在RecSys/Search/Ads中的具体应用潜力，更像是通用算法发现工具。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:03:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08522v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08522v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models have made significant progress in complex but easy-to-verify problems, yet they still struggle with discovering the unknown. In this paper, we present \textbf{AlphaResearch}, an autonomous research agent designed to discover new algorithms on open-ended problems. To synergize the feasibility and innovation of the discovery process, we construct a novel dual research environment by combining the execution-based verify and simulated real-world peer review environment. AlphaResearch discovers new algorithm by iteratively running the following steps: (1) propose new ideas (2) verify the ideas in the dual research environment (3) optimize the research proposals for better performance. To promote a transparent evaluation process, we construct \textbf{AlphaResearchComp}, a new evaluation benchmark that includes an eight open-ended algorithmic problems competition, with each problem carefully curated and verified through executable pipelines, objective metrics, and reproducibility checks. AlphaResearch gets a 2/8 win rate in head-to-head comparison with human researchers, demonstrate the possibility of accelerating algorithm discovery with LLMs. Notably, the algorithm discovered by AlphaResearch on the \emph{``packing circles''} problem achieves the best-of-known performance, surpassing the results of human researchers and strong baselines from recent work (e.g., AlphaEvolve). Additionally, we conduct a comprehensive analysis of the remaining challenges of the 6/8 failure cases, providing valuable insights for future research.
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            <a href="https://www.alphaxiv.org/abs/2511.08455v1" target="_blank" rel="noopener noreferrer">
                Bot Meets Shortcut：LLM如何辅助处理未知不变性分布外场景？
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shiyan Zheng, Herun Wan, Minnan Luo, Junhang Huang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在处理分布外泛化问题中的应用，特别是未知不变性场景。虽然涉及LLM技术，但其核心焦点是机器学习泛化问题，而非RecSys/Search/Ads领域的特定应用。论文可能对推荐系统中的分布偏移问题有间接启发，但缺乏明确的领域相关性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:56:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08455v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08455v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While existing social bot detectors perform well on benchmarks, their robustness across diverse real-world scenarios remains limited due to unclear ground truth and varied misleading cues. In particular, the impact of shortcut learning, where models rely on spurious correlations instead of capturing causal task-relevant features, has received limited attention. To address this gap, we conduct an in-depth study to assess how detectors are influenced by potential shortcuts based on textual features, which are most susceptible to manipulation by social bots. We design a series of shortcut scenarios by constructing spurious associations between user labels and superficial textual cues to evaluate model robustness. Results show that shifts in irrelevant feature distributions significantly degrade social bot detector performance, with an average relative accuracy drop of 32\% in the baseline models. To tackle this challenge, we propose mitigation strategies based on large language models, leveraging counterfactual data augmentation. These methods mitigate the problem from data and model perspectives across three levels, including data distribution at both the individual user text and overall dataset levels, as well as the model's ability to extract causal information. Our strategies achieve an average relative performance improvement of 56\% under shortcut scenarios.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08199v1" target="_blank" rel="noopener noreferrer">
                句法类别是否有助于基于发展动机的课程学习在语言模型中的发展？
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arzu Burcu Güven, Anna Rogers, Rob van der Goot
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语言模型的课程学习方法和句法分析，属于NLP训练优化范畴。虽然课程学习可能对LLM训练效率有一般性改进，但论文聚焦于发展动机和句法类别等语言学特定问题，与推荐系统、搜索或广告的核心技术需求关联较弱，潜在应用不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:03:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08199v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08199v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We examine the syntactic properties of BabyLM corpus, and age-groups within CHILDES. While we find that CHILDES does not exhibit strong syntactic differentiation by age, we show that the syntactic knowledge about the training data can be helpful in interpreting model performance on linguistic tasks. For curriculum learning, we explore developmental and several alternative cognitively inspired curriculum approaches. We find that some curricula help with reading tasks, but the main performance improvement come from using the subset of syntactically categorizable data, rather than the full noisy corpus.
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            <a href="https://www.alphaxiv.org/abs/2511.07998v1" target="_blank" rel="noopener noreferrer">
                结构化数据问答的自校正蒸馏
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Self-Correction Distillation for Structured Data Question Answering
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yushan Zhu, Wen Zhang, Long Jin, Mengshu Sun, Ling Zhong, Zhiqiang Liu, Juan Li,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注结构化数据问答的自校正蒸馏技术，属于特定领域的问答应用。虽然蒸馏技术可能对模型效率有贡献，但论文没有明确展示其在推荐系统、搜索或广告中的直接应用潜力。核心焦点是问答任务而非推荐/搜索的核心排名或匹配问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:01:51
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                <a href="https://arxiv.org/abs/2511.07998v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07998v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries. To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM. Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.
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            <a href="https://www.alphaxiv.org/abs/2511.07885v1" target="_blank" rel="noopener noreferrer">
                智能每瓦：衡量本地人工智能的智能效率
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        <div class="mb-2 text-base text-gray-700">
            Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Sh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注AI效率度量，与'Enabling LLM Tech'中的效率趋势有一定关联，可能涉及模型压缩或高效推理技术。在推荐系统、搜索和广告中，高效的本地AI可以支持边缘设备上的个性化服务、低延迟推荐和隐私保护计算。但论文焦点是通用的效率度量而非具体的Transformer架构改进或直接应用，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:33:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07885v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07885v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DC</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Rapidly growing demand strains this paradigm, and cloud providers struggle to scale infrastructure at pace. Two advances enable us to rethink this paradigm: small LMs (<=20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) run these models at interactive latencies. This raises the question: can local inference viably redistribute demand from centralized infrastructure? Answering this requires measuring whether local LMs can accurately answer real-world queries and whether they can do so efficiently enough to be practical on power-constrained devices (i.e., laptops). We propose intelligence per watt (IPW), task accuracy divided by unit of power, as a metric for assessing capability and efficiency of local inference across model-accelerator pairs. We conduct a large-scale empirical study across 20+ state-of-the-art local LMs, 8 accelerators, and a representative subset of LLM traffic: 1M real-world single-turn chat and reasoning queries. For each query, we measure accuracy, energy, latency, and power. Our analysis reveals $3$ findings. First, local LMs can accurately answer 88.7% of single-turn chat and reasoning queries with accuracy varying by domain. Second, from 2023-2025, IPW improved 5.3x and local query coverage rose from 23.2% to 71.3%. Third, local accelerators achieve at least 1.4x lower IPW than cloud accelerators running identical models, revealing significant headroom for optimization. These findings demonstrate that local inference can meaningfully redistribute demand from centralized infrastructure, with IPW serving as the critical metric for tracking this transition. We release our IPW profiling harness for systematic intelligence-per-watt benchmarking.
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            <a href="https://www.alphaxiv.org/abs/2511.07879v1" target="_blank" rel="noopener noreferrer">
                基于新闻文章中未来提及和相关实体提取的计划事件预测
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            Planned Event Forecasting using Future Mentions and Related Entity Extraction in News Articles
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Neelesh Kumar Shukla, Pranay Sanghvi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注新闻文章中的事件预测，虽然涉及信息提取和预测建模，但缺乏与推荐系统、搜索或广告领域的直接联系。未来提及和实体提取技术可能对内容理解有一般性价值，但论文标题未表明这些技术如何具体应用于RecSys/Search/Ads的核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:25:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07879v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07879v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    In democracies like India, people are free to express their views and demands. Sometimes this causes situations of civil unrest such as protests, rallies, and marches. These events may be disruptive in nature and are often held without prior permission from the competent authority. Forecasting these events helps administrative officials take necessary action. Usually, protests are announced well in advance to encourage large participation. Therefore, by analyzing such announcements in news articles, planned events can be forecasted beforehand. We developed such a system in this paper to forecast social unrest events using topic modeling and word2vec to filter relevant news articles, and Named Entity Recognition (NER) methods to identify entities such as people, organizations, locations, and dates. Time normalization is applied to convert future date mentions into a standard format. In this paper, we have developed a geographically independent, generalized model to identify key features for filtering civil unrest events. There could be many mentions of entities, but only a few may actually be involved in the event. This paper calls such entities Related Entities and proposes a method to extract them, referred to as Related Entity Extraction.
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            <a href="https://www.alphaxiv.org/abs/2511.08087v1" target="_blank" rel="noopener noreferrer">
                超越像素：基于视觉语言模型的参考引导合成中身份保持评估
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        <div class="mb-2 text-base text-gray-700">
            Beyond the Pixels: VLM-based Evaluation of Identity Preservation in Reference-Guided Synthesis
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aditi Singhania, Krutik Malani, Riddhi Dhawan, Arushi Jain, Garv Tandon, Nippun ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型在图像合成中身份保持的评估，属于视觉内容生成和评估领域。虽然VLM技术有潜力应用于广告创意评估或产品图像生成的质量控制，但这与当前关注的搜索、推荐和广告排名核心算法关系较弱，且更偏向AIGC和内容生成应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:43:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08087v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08087v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Evaluating identity preservation in generative models remains a critical yet unresolved challenge. Existing metrics rely on global embeddings or coarse VLM prompting, failing to capture fine-grained identity changes and providing limited diagnostic insight. We introduce Beyond the Pixels, a hierarchical evaluation framework that decomposes identity assessment into feature-level transformations. Our approach guides VLMs through structured reasoning by (1) hierarchically decomposing subjects into (type, style) -> attribute -> feature decision tree, and (2) prompting for concrete transformations rather than abstract similarity scores. This decomposition grounds VLM analysis in verifiable visual evidence, reducing hallucinations and improving consistency. We validate our framework across four state-of-the-art generative models, demonstrating strong alignment with human judgments in measuring identity consistency. Additionally, we introduce a new benchmark specifically designed to stress-test generative models. It comprises 1,078 image-prompt pairs spanning diverse subject types, including underrepresented categories such as anthropomorphic and animated characters, and captures an average of six to seven transformation axes per prompt.
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            <a href="https://www.alphaxiv.org/abs/2511.07738v1" target="_blank" rel="noopener noreferrer">
                从探索到利用：一种用于噪声容忍多模态大语言模型训练的两阶段熵强化学习价值正则化方法
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            From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Donglai Xu, Hongzheng Yang, Yuzhi Zhao, Pingping Zhang, Jinpeng Chen, Wenao Ma, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型（MLLM）训练中的噪声容忍问题，使用强化学习方法。虽然强化学习可能应用于推荐系统，但论文标题明确聚焦于多模态模型训练而非RecSys/Search/Ads领域的直接应用。标题中未表明该方法在推荐、搜索或广告中的具体应用潜力，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 01:42:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07738v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07738v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reinforcement Learning with Verifiable Rewards (RLVR) for Multimodal Large Language Models (MLLMs) is highly dependent on high-quality labeled data, which is often scarce and prone to substantial annotation noise in real-world scenarios. Existing unsupervised RLVR methods, including pure entropy minimization, can overfit to incorrect labels and limit the crucial reward ranking signal for Group-Relative Policy Optimization (GRPO). To address these challenges and enhance noise tolerance, we propose a novel two-stage, token-level entropy optimization method for RLVR. This approach dynamically guides the model from exploration to exploitation during training. In the initial exploration phase, token-level entropy maximization promotes diverse and stochastic output generation, serving as a strong regularizer that prevents premature convergence to noisy labels and ensures sufficient intra-group variation, which enables more reliable reward gradient estimation in GRPO. As training progresses, the method transitions into the exploitation phase, where token-level entropy minimization encourages the model to produce confident and deterministic outputs, thereby consolidating acquired knowledge and refining prediction accuracy. Empirically, across three MLLM backbones - Qwen2-VL-2B, Qwen2-VL-7B, and Qwen2.5-VL-3B - spanning diverse noise settings and multiple tasks, our phased strategy consistently outperforms prior approaches by unifying and enhancing external, internal, and entropy-based methods, delivering robust and superior performance across the board.
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            <a href="https://www.alphaxiv.org/abs/2511.08152v1" target="_blank" rel="noopener noreferrer">
                Boomda：面向多模态领域自适应的平衡多目标优化
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Boomda: Balanced Multi-objective Optimization for Multimodal Domain Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jun Sun, Xinxin Zhang, Simin Hong, Jian Zhu, Xiang Gao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多模态领域自适应，这与VLM类比异构数据的理念有一定关联，但标题未明确说明与推荐系统、搜索或广告的具体联系。多目标优化技术可能应用于平衡推荐系统中的多个业务指标，但缺乏明确的适用场景说明，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:03:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08152v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08152v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal learning, while contributing to numerous success stories across various fields, faces the challenge of prohibitively expensive manual annotation. To address the scarcity of annotated data, a popular solution is unsupervised domain adaptation, which has been extensively studied in unimodal settings yet remains less explored in multimodal settings. In this paper, we investigate heterogeneous multimodal domain adaptation, where the primary challenge is the varying domain shifts of different modalities from the source to the target domain. We first introduce the information bottleneck method to learn representations for each modality independently, and then match the source and target domains in the representation space with correlation alignment. To balance the domain alignment of all modalities, we formulate the problem as a multi-objective task, aiming for a Pareto optimal solution. By exploiting the properties specific to our model, the problem can be simplified to a quadratic programming problem. Further approximation yields a closed-form solution, leading to an efficient modality-balanced multimodal domain adaptation algorithm. The proposed method features \textbf{B}alanced multi-\textbf{o}bjective \textbf{o}ptimization for \textbf{m}ultimodal \textbf{d}omain \textbf{a}daptation, termed \textbf{Boomda}. Extensive empirical results showcase the effectiveness of the proposed approach and demonstrate that Boomda outperforms the competing schemes. The code is is available at: https://github.com/sunjunaimer/Boomda.git.
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            <a href="https://www.alphaxiv.org/abs/2511.08003v1" target="_blank" rel="noopener noreferrer">
                VideoLLMs的锐利眼睛与记忆：面向高效可靠视频LLM推理的信息感知视觉令牌剪枝
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Sharp Eyes and Memory for VideoLLMs: Information-Aware Visual Token Pruning for Efficient and Reliable VideoLLM Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jialong Qin, Xin Zou, Di Lu, Yibo Yan, Xuming Hu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频LLM的视觉令牌剪枝技术，属于视觉模态的效率优化。虽然Transformer效率改进可能间接应用于推荐系统，但论文明确聚焦于视频理解而非RecSys/Search/Ads的核心问题。其技术路径与处理异构数据模态的统一建模关联较弱，应用场景过于特定。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:07:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08003v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08003v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Current Video Large Language Models (VideoLLMs) suffer from quadratic computational complexity and key-value cache scaling, due to their reliance on processing excessive redundant visual tokens. To address this problem, we propose SharpV, a minimalist and efficient method for adaptive pruning of visual tokens and KV cache. Different from most uniform compression approaches, SharpV dynamically adjusts pruning ratios based on spatial-temporal information. Remarkably, this adaptive mechanism occasionally achieves performance gains over dense models, offering a novel paradigm for adaptive pruning. During the KV cache pruning stage, based on observations of visual information degradation, SharpV prunes degraded visual features via a self-calibration manner, guided by similarity to original visual features. In this way, SharpV achieves hierarchical cache pruning from the perspective of information bottleneck, offering a new insight into VideoLLMs' information flow. Experiments on multiple public benchmarks demonstrate the superiority of SharpV. Moreover, to the best of our knowledge, SharpV is notably the first two-stage pruning framework that operates without requiring access to exposed attention scores, ensuring full compatibility with hardware acceleration techniques like Flash Attention.
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            <a href="https://www.alphaxiv.org/abs/2511.07877v1" target="_blank" rel="noopener noreferrer">
                视觉桥梁：通用视觉感知表征生成
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Visual Bridge: Universal Visual Perception Representations Generating
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yilin Gao, Shuguang Dou, Junzhou Li, Zhiheng Yu, Yin Li, Dongsheng Jiang, Shugon...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉表征生成，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然视觉表征在商品推荐或广告创意中有潜在应用，但论文标题未明确指向这些领域的具体应用场景，且可能更偏向纯粹的视觉技术研究。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:25:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07877v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07877v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in diffusion models have achieved remarkable success in isolated computer vision tasks such as text-to-image generation, depth estimation, and optical flow. However, these models are often restricted by a ``single-task-single-model'' paradigm, severely limiting their generalizability and scalability in multi-task scenarios. Motivated by the cross-domain generalization ability of large language models, we propose a universal visual perception framework based on flow matching that can generate diverse visual representations across multiple tasks. Our approach formulates the process as a universal flow-matching problem from image patch tokens to task-specific representations rather than an independent generation or regression problem. By leveraging a strong self-supervised foundation model as the anchor and introducing a multi-scale, circular task embedding mechanism, our method learns a universal velocity field to bridge the gap between heterogeneous tasks, supporting efficient and flexible representation transfer. Extensive experiments on classification, detection, segmentation, depth estimation, and image-text retrieval demonstrate that our model achieves competitive performance in both zero-shot and fine-tuned settings, outperforming prior generalist and several specialist models. Ablation studies further validate the robustness, scalability, and generalization of our framework. Our work marks a significant step towards general-purpose visual perception, providing a solid foundation for future research in universal vision modeling.
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            <a href="https://www.alphaxiv.org/abs/2511.08029v1" target="_blank" rel="noopener noreferrer">
                BiCA：通过引文感知困难负样本实现有效的生物医学密集检索
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            BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aarush Sinha, Pavan Kumar S, Roshan Balaji, Nirav Pravinbhai Bhatt
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物医学领域的密集检索技术，这是一个特定的垂直领域应用，与推荐系统、搜索或广告的核心领域进展无关。虽然密集检索技术本身在搜索中有应用潜力，但论文明确限定在生物医学领域，且没有证据表明其方法可以推广到更广泛的RecSys/Search/Ads场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:31:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08029v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08029v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.CL</span></div>
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                    Hard negatives are essential for training effective retrieval models. Hard-negative mining typically relies on ranking documents using cross-encoders or static embedding models based on similarity metrics such as cosine distance. Hard negative mining becomes challenging for biomedical and scientific domains due to the difficulty in distinguishing between source and hard negative documents. However, referenced documents naturally share contextual relevance with the source document but are not duplicates, making them well-suited as hard negatives. In this work, we propose BiCA: Biomedical Dense Retrieval with Citation-Aware Hard Negatives, an approach for hard-negative mining by utilizing citation links in 20,000 PubMed articles for improving a domain-specific small dense retriever. We fine-tune the GTE_small and GTE_Base models using these citation-informed negatives and observe consistent improvements in zero-shot dense retrieval using nDCG@10 for both in-domain and out-of-domain tasks on BEIR and outperform baselines on long-tailed topics in LoTTE using Success@5. Our findings highlight the potential of leveraging document link structure to generate highly informative negatives, enabling state-of-the-art performance with minimal fine-tuning and demonstrating a path towards highly data-efficient domain adaptation.
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            <a href="https://www.alphaxiv.org/abs/2511.08376v1" target="_blank" rel="noopener noreferrer">
                TurkEmbed：基于自然语言推理与语义文本相似度任务的土耳其语嵌入模型
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            <i class="fa fa-star mr-1"></i>2/10
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            TurkEmbed: Turkish Embedding Model on NLI & STS Tasks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Özay Ezerceli, Gizem Gümüşçekiçci, Tuğba Erkoç, Berke Özenç
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于土耳其语嵌入模型在NLI和STS任务上的开发，属于特定语言的NLP技术研究。虽然嵌入模型是推荐和搜索系统的底层技术组件，但该工作局限于单一语言且未明确展示在推荐/搜索/广告领域的应用潜力。缺乏对多模态数据、序列建模或推荐系统特定问题的针对性，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:54:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08376v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08376v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper introduces TurkEmbed, a novel Turkish language embedding model designed to outperform existing models, particularly in Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. Current Turkish embedding models often rely on machine-translated datasets, potentially limiting their accuracy and semantic understanding. TurkEmbed utilizes a combination of diverse datasets and advanced training techniques, including matryoshka representation learning, to achieve more robust and accurate embeddings. This approach enables the model to adapt to various resource-constrained environments, offering faster encoding capabilities. Our evaluation on the Turkish STS-b-TR dataset, using Pearson and Spearman correlation metrics, demonstrates significant improvements in semantic similarity tasks. Furthermore, TurkEmbed surpasses the current state-of-the-art model, Emrecan, on All-NLI-TR and STS-b-TR benchmarks, achieving a 1-4\% improvement. TurkEmbed promises to enhance the Turkish NLP ecosystem by providing a more nuanced understanding of language and facilitating advancements in downstream applications.
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            <a href="https://www.alphaxiv.org/abs/2511.08392v1" target="_blank" rel="noopener noreferrer">
                PCRLLM：在逐步逻辑约束下使用大型语言模型进行证明携带推理
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            <i class="fa fa-star mr-1"></i>2/10
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            PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tangrui Li, Pei Wang, Hongzheng Wang Christian Hahm, Matteo Spatola, Justin Shi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的逻辑推理和证明验证能力，属于核心NLP推理任务。虽然涉及LLM技术，但其应用场景偏向形式化验证和逻辑推理系统，与推荐系统、搜索或广告领域的实际应用关联度较低。论文未明确展示在RecSys/Search/Ads中的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:10:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08392v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08392v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) often exhibit limited logical coherence, mapping premises to conclusions without adherence to explicit inference rules. We propose Proof-Carrying Reasoning with LLMs (PCRLLM), a framework that constrains reasoning to single-step inferences while preserving natural language formulations. Each output explicitly specifies premises, rules, and conclusions, thereby enabling verification against a target logic. This mechanism mitigates trustworthiness concerns by supporting chain-level validation even in black-box settings. Moreover, PCRLLM facilitates systematic multi-LLM collaboration, allowing intermediate steps to be compared and integrated under formal rules. Finally, we introduce a benchmark schema for generating large-scale step-level reasoning data, combining natural language expressiveness with formal rigor.
                </div>
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            <a href="https://www.alphaxiv.org/abs/2511.08126v1" target="_blank" rel="noopener noreferrer">
                多模态大语言模型中的量化与物体感知偏离人类语言认知
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Quantification and object perception in Multimodal Large Language Models deviate from human linguistic cognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Raquel Montero, Natalia Moskvina, Paolo Morosi, Tamara Serrano, Elena Pagliarini...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究多模态LLM在量化认知和物体感知方面与人类语言认知的偏差，属于LLM评估和认知科学交叉领域。虽然涉及多模态模型，但焦点是认知偏差而非技术改进，且没有明确指向推荐系统、搜索或广告的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:30:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08126v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08126v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Quantification has been proven to be a particularly difficult linguistic phenomenon for (Multimodal) Large Language Models (MLLMs). However, given that quantification interfaces with the logic, pragmatic, and numerical domains, the exact reasons for the poor performance are still unclear. This papers looks at three key features of human quantification shared cross-linguistically that have remained so far unexplored in the (M)LLM literature: the ordering of quantifiers into scales, the ranges of use and prototypicality, and the biases inherent in the human approximate number system. The aim is to determine how these features are encoded in the models' architecture, how they may differ from humans, and whether the results are affected by the type of model and language under investigation. We find that there are clear differences between humans and MLLMs with respect to these features across various tasks that tap into the representation of quantification in vivo vs. in silico. This work, thus, paves the way for addressing the nature of MLLMs as semantic and pragmatic agents, while the cross-linguistic lens can elucidate whether their abilities are robust and stable across different languages.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07865v1" target="_blank" rel="noopener noreferrer">
                基于大语言模型的全自动混沌工程：面向低成本构建弹性软件系统
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注软件系统弹性和混沌工程，属于系统可靠性领域，与推荐系统、搜索或广告的核心技术方向关联度很低。虽然提到了LLM技术，但其应用场景是软件工程而非推荐/搜索/广告领域，无法看出在推荐系统、搜索或广告中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:03:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07865v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07865v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SE</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.08537v1" target="_blank" rel="noopener noreferrer">
                从语义角色到观点角色：面向低资源观点角色标注的多任务与迁移学习的语义角色标注数据提取
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        <div class="mb-2 text-base text-gray-700">
            From Semantic Roles to Opinion Roles: SRL Data Extraction for Multi-Task and Transfer Learning in Low-Resource ORL
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amirmohammad Omidi Galdiani, Sepehr Rezaei Melal, Mohammad Norasteh, Arash Youse...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语义角色标注(SRL)和观点角色标注(ORL)之间的迁移学习，属于NLP中的语义理解任务。虽然观点分析在推荐和搜索中有潜在应用（如评论情感分析），但论文聚焦于低资源场景下的标注数据提取和多任务学习，与推荐系统、搜索广告的核心技术关联较弱，且未明确涉及Transformer架构创新或LLM应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:17:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08537v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08537v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This report presents a detailed methodology for constructing a high-quality Semantic Role Labeling (SRL) dataset from the Wall Street Journal (WSJ) portion of the OntoNotes 5.0 corpus and adapting it for Opinion Role Labeling (ORL) tasks. Leveraging the PropBank annotation framework, we implement a reproducible extraction pipeline that aligns predicate-argument structures with surface text, converts syntactic tree pointers to coherent spans, and applies rigorous cleaning to ensure semantic fidelity. The resulting dataset comprises 97,169 predicate-argument instances with clearly defined Agent (ARG0), Predicate (REL), and Patient (ARG1) roles, mapped to ORL's Holder, Expression, and Target schema. We provide a detailed account of our extraction algorithms, discontinuous argument handling, annotation corrections, and statistical analysis of the resulting dataset. This work offers a reusable resource for researchers aiming to leverage SRL for enhancing ORL, especially in low-resource opinion mining scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.08143v1" target="_blank" rel="noopener noreferrer">
                关系作为先验：基于大语言模型的文档级关系抽取新范式
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            Relation as a Prior: A Novel Paradigm for LLM-based Document-level Relation Extraction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiankun Pi, Yepeng Sun, Jicang Lu, Qinlong Fan, Ningbo Huang, Shiyu Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文档级关系抽取这一特定NLP任务，属于纯粹的信息提取应用。虽然涉及LLM技术，但其应用场景局限于关系抽取，与推荐系统、搜索或广告中的排序、匹配、用户建模等核心问题没有直接关联，也没有展示在异构数据处理方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:55:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08143v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08143v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Large Language Models (LLMs) have demonstrated their remarkable capabilities in document understanding. However, recent research reveals that LLMs still exhibit performance gaps in Document-level Relation Extraction (DocRE) as requiring fine-grained comprehension. The commonly adopted "extract entities then predict relations" paradigm in LLM-based methods leads to these gaps due to two main reasons: (1) Numerous unrelated entity pairs introduce noise and interfere with the relation prediction for truly related entity pairs. (2) Although LLMs have identified semantic associations between entities, relation labels beyond the predefined set are still treated as prediction errors. To address these challenges, we propose a novel Relation as a Prior (RelPrior) paradigm for LLM-based DocRE. For challenge (1), RelPrior utilizes binary relation as a prior to extract and determine whether two entities are correlated, thereby filtering out irrelevant entity pairs and reducing prediction noise. For challenge (2), RelPrior utilizes predefined relation as a prior to match entities for triples extraction instead of directly predicting relation. Thus, it avoids misjudgment caused by strict predefined relation labeling. Extensive experiments on two benchmarks demonstrate that RelPrior achieves state-of-the-art performance, surpassing existing LLM-based methods.
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            <a href="https://www.alphaxiv.org/abs/2511.08017v1" target="_blank" rel="noopener noreferrer">
                HyCoRA：面向角色扮演的超对比角色自适应学习
            </a>
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        <div class="mb-2 text-base text-gray-700">
            HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shihao Yang, Zhicong Lu, Yong Yang, Bo Lv, Yang Shen, Nayu Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及对比学习和自适应学习技术，但其核心应用场景是角色扮演，属于对话系统和内容生成领域。这些技术虽然可能间接应用于推荐或搜索中的用户建模，但论文本身没有明确指向RecSys/Search/Ads应用，且角色扮演更接近纯粹的LLM对话应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:18:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08017v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08017v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or assign a separate parameterized module to each role. However, the role-shared module may ignore distinct traits of each role, weakening personality learning, while the role-specific module may overlook shared traits across multiple roles, hindering commonality modeling. In this paper, we propose a novel HyCoRA: Hyper-Contrastive Role-Adaptive learning framework, which efficiently improves multi-character role-playing ability by balancing the learning of distinct and shared traits. Specifically, we propose a Hyper-Half Low-Rank Adaptation structure, where one half is a role-specific module generated by a lightweight hyper-network, and the other half is a trainable role-shared module. The role-specific module is devised to represent distinct persona signatures, while the role-shared module serves to capture common traits. Moreover, to better reflect distinct personalities across different roles, we design a hyper-contrastive learning mechanism to help the hyper-network distinguish their unique characteristics. Extensive experimental results on both English and Chinese available benchmarks demonstrate the superiority of our framework. Further GPT-4 evaluations and visual analyses also verify the capability of HyCoRA to capture role characteristics.
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            <a href="https://www.alphaxiv.org/abs/2511.08317v1" target="_blank" rel="noopener noreferrer">
                基于LLM模拟审稿人-作者辩论的异质图推理自动论文评审
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            Automatic Paper Reviewing with Heterogeneous Graph Reasoning over LLM-Simulated Reviewer-Author Debates
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuaimin Li, Liyang Fan, Yufang Lin, Zeyang Li, Xian Wei, Shiwen Ni, Hamid Aline...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注自动论文评审系统，属于学术出版领域的特定应用，与推荐系统、搜索或广告的核心技术无关。虽然涉及异质图推理和LLM技术，但应用场景过于狭窄，缺乏在RecSys/Search/Ads领域的直接或潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:46:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08317v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08317v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing paper review methods often rely on superficial manuscript features or directly on large language models (LLMs), which are prone to hallucinations, biased scoring, and limited reasoning capabilities. Moreover, these methods often fail to capture the complex argumentative reasoning and negotiation dynamics inherent in reviewer-author interactions. To address these limitations, we propose ReViewGraph (Reviewer-Author Debates Graph Reasoner), a novel framework that performs heterogeneous graph reasoning over LLM-simulated multi-round reviewer-author debates. In our approach, reviewer-author exchanges are simulated through LLM-based multi-agent collaboration. Diverse opinion relations (e.g., acceptance, rejection, clarification, and compromise) are then explicitly extracted and encoded as typed edges within a heterogeneous interaction graph. By applying graph neural networks to reason over these structured debate graphs, ReViewGraph captures fine-grained argumentative dynamics and enables more informed review decisions. Extensive experiments on three datasets demonstrate that ReViewGraph outperforms strong baselines with an average relative improvement of 15.73%, underscoring the value of modeling detailed reviewer-author debate structures.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07752v1" target="_blank" rel="noopener noreferrer">
                回到未来：过去与未来上下文可预测性在增量语言生成中的作用
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Back to the Future: The Role of Past and Future Context Predictability in Incremental Language Production
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shiva Upadhye, Richard Futrell
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究人类语言生成过程中的认知机制和上下文预测，属于基础语言学和心理语言学范畴。虽然涉及语言生成，但其焦点是理解人类认知过程而非开发可应用于推荐系统、搜索或广告的技术模型。论文缺乏明确的Transformer架构改进或LLM技术应用潜力，与当前关注的技术驱动研究方向相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 02:09:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07752v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07752v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Contextual predictability shapes both the form and choice of words in online language production. The effects of the predictability of a word given its previous context are generally well-understood in both production and comprehension, but studies of naturalistic production have also revealed a poorly-understood backward predictability effect of a word given its future context, which may be related to future planning. Here, in two studies of naturalistic speech corpora, we investigate backward predictability effects using improved measures and more powerful language models, introducing a new principled and conceptually motivated information-theoretic predictability measure that integrates predictability from both the future and the past context. Our first study revisits classic predictability effects on word duration. Our second study investigates substitution errors within a generative framework that independently models the effects of lexical, contextual, and communicative factors on word choice, while predicting the actual words that surface as speech errors. We find that our proposed conceptually-motivated alternative to backward predictability yields qualitatively similar effects across both studies. Through a fine-grained analysis of substitution errors, we further show that different kinds of errors are suggestive of how speakers prioritize form, meaning, and context-based information during lexical planning. Together, these findings illuminate the functional roles of past and future context in how speakers encode and choose words, offering a bridge between contextual predictability effects and the mechanisms of sentence planning.
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            <a href="https://www.alphaxiv.org/abs/2511.08204v1" target="_blank" rel="noopener noreferrer">
                基于随机采样的编码器微调在天文学知识提取中优于开源权重GPT模型
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Encoder Fine-tuning with Stochastic Sampling Outperforms Open-weight GPT in Astronomy Knowledge Extraction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shivam Rawat, Lucie Flek, Akbar Karimi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于天文学领域的特定知识提取应用，这属于明确的领域特定应用，属于被排除的无关主题。虽然提到了GPT模型和微调技术，但其核心焦点是天文学而非推荐系统、搜索或广告领域。论文没有展示任何在推荐系统、搜索或广告方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:08:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08204v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08204v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Scientific literature in astronomy is rapidly expanding, making it increasingly important to automate the extraction of key entities and contextual information from research papers. In this paper, we present an encoder-based system for extracting knowledge from astronomy articles. Our objective is to develop models capable of classifying telescope references, detecting auxiliary semantic attributes, and recognizing instrument mentions from textual content. To this end, we implement a multi-task transformer-based system built upon the SciBERT model and fine-tuned for astronomy corpora classification. To carry out the fine-tuning, we stochastically sample segments from the training data and use majority voting over the test segments at inference time. Our system, despite its simplicity and low-cost implementation, significantly outperforms the open-weight GPT baseline.
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            <a href="https://www.alphaxiv.org/abs/2511.08109v1" target="_blank" rel="noopener noreferrer">
                疏离预测：使用掩码语言建模衡量语义类别中断
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            Estranged Predictions: Measuring Semantic Category Disruption with Masked Language Modelling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注使用掩码语言建模测量语义类别中断，这属于语言模型评估和语义分析范畴。虽然涉及语言模型技术，但该方法主要针对语言理解评估，与推荐系统、搜索或广告中的核心问题（如排序、用户建模、特征工程）缺乏直接关联。在推荐/搜索/广告领域，语义中断测量可能有助于内容质量评估，但应用潜力有限且非主要研究方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:07:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08109v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08109v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This paper examines how science fiction destabilises ontological categories by measuring conceptual permeability across the terms human, animal, and machine using masked language modelling (MLM). Drawing on corpora of science fiction (Gollancz SF Masterworks) and general fiction (NovelTM), we operationalise Darko Suvin's theory of estrangement as computationally measurable deviation in token prediction, using RoBERTa to generate lexical substitutes for masked referents and classifying them via Gemini. We quantify conceptual slippage through three metrics: retention rate, replacement rate, and entropy, mapping the stability or disruption of category boundaries across genres. Our findings reveal that science fiction exhibits heightened conceptual permeability, particularly around machine referents, which show significant cross-category substitution and dispersion. Human terms, by contrast, maintain semantic coherence and often anchor substitutional hierarchies. These patterns suggest a genre-specific restructuring within anthropocentric logics. We argue that estrangement in science fiction operates as a controlled perturbation of semantic norms, detectable through probabilistic modelling, and that MLMs, when used critically, serve as interpretive instruments capable of surfacing genre-conditioned ontological assumptions. This study contributes to the methodological repertoire of computational literary studies and offers new insights into the linguistic infrastructure of science fiction.
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            <a href="https://www.alphaxiv.org/abs/2511.08349v1" target="_blank" rel="noopener noreferrer">
                混合量子-经典选择性状态空间人工智能
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        <div class="mb-2 text-base text-gray-700">
            Hybrid Quantum-Classical Selective State Space Artificial Intelligence
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amin Ebrahimi, Farzan Haddadi
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及量子-经典混合计算和状态空间模型，这些是前沿AI技术，但未明确展示与推荐系统、搜索或广告的直接关联。虽然状态空间模型可能用于序列建模，但量子计算在当前阶段对大规模推荐系统的实际应用尚不明确，且论文未提及具体的Transformer架构改进或LLM技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:26:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08349v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08349v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">quant-ph</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Hybrid Quantum Classical (HQC) algorithms constitute one of the most effective paradigms for exploiting the computational advantages of quantum systems in large-scale numerical tasks. By operating in high-dimensional Hilbert spaces, quantum circuits enable exponential speed-ups and provide access to richer representations of cost landscapes compared to purely classical methods. These capabilities are particularly relevant for machine learning, where state-of-the-art models especially in Natural Language Processing (NLP) suffer from prohibitive time complexity due to massive matrix multiplications and high-dimensional optimization. In this manuscript, we propose a Hybrid Quantum Classical selection mechanism for the Mamba architecture, designed specifically for temporal sequence classification problems. Our approach leverages Variational Quantum Circuits (VQCs) as quantum gating modules that both enhance feature extraction and improve suppression of irrelevant information. This integration directly addresses the computational bottlenecks of deep learning architectures by exploiting quantum resources for more efficient representation learning. We analyze how introducing quantum subroutines into large language models (LLMs) impacts their generalization capability, expressivity, and parameter efficiency. The results highlight the potential of quantum-enhanced gating mechanisms as a path toward scalable, resource-efficient NLP models, in a limited simulation step. Within the first four epochs on a reshaped MNIST dataset with input format (batch, 784, d_model), our hybrid model achieved 24.6% accuracy while using one quantum layer and achieve higher expressivity, compared to 21.6% obtained by a purely classical selection mechanism. we state No founding
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            <a href="https://www.alphaxiv.org/abs/2511.08151v1" target="_blank" rel="noopener noreferrer">
                SciAgent：面向通用科学推理的统一多智能体系统
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            SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuchen Li, Ruitao Wu, Xuanbo Liu, Xukai Wang, Jinbo Hu, Zhixin Bai, Bohan Zeng, ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于科学领域的多智能体推理系统，属于特定领域应用而非通用推荐/搜索/广告技术。虽然多智能体架构在理论上可能应用于复杂决策场景，但论文明确限定在科学推理领域，缺乏与推荐系统、搜索或广告的直接关联或潜在应用路径。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:00:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08151v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08151v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span></div>
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                    Recent advances in large language models have enabled AI systems to achieve expert-level performance on domain-specific scientific tasks, yet these systems remain narrow and handcrafted. We introduce SciAgent, a unified multi-agent system designed for generalistic scientific reasoning-the ability to adapt reasoning strategies across disciplines and difficulty levels. SciAgent organizes problem solving as a hierarchical process: a Coordinator Agent interprets each problem's domain and complexity, dynamically orchestrating specialized Worker Systems, each composed of interacting reasoning Sub-agents for symbolic deduction, conceptual modeling, numerical computation, and verification. These agents collaboratively assemble and refine reasoning pipelines tailored to each task. Across mathematics and physics Olympiads (IMO, IMC, IPhO, CPhO), SciAgent consistently attains or surpasses human gold-medalist performance, demonstrating both domain generality and reasoning adaptability. Additionally, SciAgent has been tested on the International Chemistry Olympiad (IChO) and selected problems from the Humanity's Last Exam (HLE) benchmark, further confirming the system's ability to generalize across diverse scientific domains. This work establishes SciAgent as a concrete step toward generalistic scientific intelligence-AI systems capable of coherent, cross-disciplinary reasoning at expert levels.
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            <a href="https://www.alphaxiv.org/abs/2511.08139v1" target="_blank" rel="noopener noreferrer">
                关于位置编码、形态复杂性与词序灵活性之间相互作用的研究
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            On the Interplay between Positional Encodings, Morphological Complexity, and Word Order Flexibility
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kushal Tatariya, Wessel Poelman, Miryam de Lhoneux
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究位置编码与语言形态特性的相互作用，属于NLP基础研究范畴。虽然位置编码是Transformer架构的核心组件，但论文聚焦于语言学特性（形态复杂性、词序灵活性）而非架构效率改进或RecSys/Search/Ads应用，与当前关注点的直接关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:50:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08139v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08139v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Language model architectures are predominantly first created for English and subsequently applied to other languages. It is an open question whether this architectural bias leads to degraded performance for languages that are structurally different from English. We examine one specific architectural choice: positional encodings, through the lens of the trade-off hypothesis: the supposed interplay between morphological complexity and word order flexibility. This hypothesis posits a trade-off between the two: a more morphologically complex language can have a more flexible word order, and vice-versa. Positional encodings are a direct target to investigate the implications of this hypothesis in relation to language modelling. We pretrain monolingual model variants with absolute, relative, and no positional encodings for seven typologically diverse languages and evaluate them on four downstream tasks. Contrary to previous findings, we do not observe a clear interaction between position encodings and morphological complexity or word order flexibility, as measured by various proxies. Our results show that the choice of tasks, languages, and metrics are essential for drawing stable conclusions
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            <a href="https://www.alphaxiv.org/abs/2511.07989v1" target="_blank" rel="noopener noreferrer">
                南斯拉夫语言文本分类的现状：微调还是提示学习？
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            State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taja Kuzman Pungeršek, Peter Rupnik, Ivan Porupski, Vuk Dinić, Nikola Ljubešić
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定语言（南斯拉夫语言）的文本分类方法比较，属于纯NLP应用领域。虽然涉及微调和提示学习等LLM技术，但缺乏与推荐系统、搜索或广告领域的明确关联，也没有展示这些技术如何应用于异构数据建模或多模态学习场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:54:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07989v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07989v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Until recently, fine-tuned BERT-like models provided state-of-the-art performance on text classification tasks. With the rise of instruction-tuned decoder-only models, commonly known as large language models (LLMs), the field has increasingly moved toward zero-shot and few-shot prompting. However, the performance of LLMs on text classification, particularly on less-resourced languages, remains under-explored. In this paper, we evaluate the performance of current language models on text classification tasks across several South Slavic languages. We compare openly available fine-tuned BERT-like models with a selection of open-source and closed-source LLMs across three tasks in three domains: sentiment classification in parliamentary speeches, topic classification in news articles and parliamentary speeches, and genre identification in web texts. Our results show that LLMs demonstrate strong zero-shot performance, often matching or surpassing fine-tuned BERT-like models. Moreover, when used in a zero-shot setup, LLMs perform comparably in South Slavic languages and English. However, we also point out key drawbacks of LLMs, including less predictable outputs, significantly slower inference, and higher computational costs. Due to these limitations, fine-tuned BERT-like models remain a more practical choice for large-scale automatic text annotation.
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            <a href="https://www.alphaxiv.org/abs/2511.07982v1" target="_blank" rel="noopener noreferrer">
                NOTAM-Evolve：一种基于LLM的知识引导自进化优化框架，用于NOTAM解读
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            NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maoqi Liu, Quan Fang, Yuhao Wu, Can Zhao, Yang Yang, Kaiquan Cai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及LLM技术，但其应用领域NOTAM（航行通告）属于航空交通管制专业领域，与推荐系统、搜索或广告的核心技术发展无关。论文标题表明这是一个特定领域的应用系统，没有显示出在推荐、搜索或广告领域的技术迁移潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:46:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07982v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07982v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate interpretation of Notices to Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to shallow parsing, failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as deep parsing, a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a large language model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state of the art on the task of structured NOTAM interpretation.
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            <a href="https://www.alphaxiv.org/abs/2511.08364v1" target="_blank" rel="noopener noreferrer">
                DPRM：多跳问答中的双重隐式过程奖励模型
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        <div class="mb-2 text-base text-gray-700">
            DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyi Wang, Yiping Song, Zhiliang Tian, Bo Liu, Tingjin Luo, Minlie Huang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多跳问答中的奖励模型设计，属于NLP领域的特定应用。虽然奖励模型技术可能间接应用于推荐系统的强化学习排序，但论文标题明确限定于问答场景，且没有表明对推荐、搜索或广告的直接相关性。这种高度领域特定的应用使其与当前关注点关联性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:41:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08364v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08364v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In multi-hop question answering (MHQA) tasks, Chain of Thought (CoT) improves the quality of generation by guiding large language models (LLMs) through multi-step reasoning, and Knowledge Graphs (KGs) reduce hallucinations via semantic matching. Outcome Reward Models (ORMs) provide feedback after generating the final answers but fail to evaluate the process for multi-step reasoning. Traditional Process Reward Models (PRMs) evaluate the reasoning process but require costly human annotations or rollout generation. While implicit PRM is trained only with outcome signals and derives step rewards through reward parameterization without explicit annotations, it is more suitable for multi-step reasoning in MHQA tasks. However, existing implicit PRM has only been explored for plain text scenarios. When adapting to MHQA tasks, it cannot handle the graph structure constraints in KGs and capture the potential inconsistency between CoT and KG paths. To address these limitations, we propose the DPRM (Dual Implicit Process Reward Model). It trains two implicit PRMs for CoT and KG reasoning in MHQA tasks. Both PRMs, namely KG-PRM and CoT-PRM, derive step-level rewards from outcome signals via reward parameterization without additional explicit annotations. Among them, KG-PRM uses preference pairs to learn structural constraints from KGs. DPRM further introduces a consistency constraint between CoT and KG reasoning steps, making the two PRMs mutually verify and collaboratively optimize the reasoning paths. We also provide a theoretical demonstration of the derivation of process rewards. Experimental results show that our method outperforms 13 baselines on multiple datasets with up to 16.6% improvement on Hit@1.
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            <a href="https://www.alphaxiv.org/abs/2511.08245v1" target="_blank" rel="noopener noreferrer">
                基于嵌入微调和检索增强生成的提示调优用于自然语言到SQL转换
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            Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jisoo Jang, Tien-Cuong Bui, Yunjun Choi, Wen-Syan Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注自然语言到SQL转换的提示调优技术，这属于特定领域的NLP应用。虽然提到了检索增强生成(RAG)和嵌入微调，但这些技术在该论文中的应用主要针对数据库查询场景，与推荐系统、搜索或广告的核心排名和建模问题缺乏直接关联。该技术可能对某些特定搜索场景有间接价值，但整体相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:41:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08245v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08245v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    This paper introduces an Error Correction through Prompt Tuning for NL-to-SQL, leveraging the latest advancements in generative pre-training-based LLMs and RAG. Our work addresses the crucial need for efficient and accurate translation of natural language queries into SQL expressions in various settings with the growing use of natural language interfaces. We explore the evolution of NLIDBs from early rule-based systems to advanced neural network-driven approaches. Drawing inspiration from the medical diagnostic process, we propose a novel framework integrating an error correction mechanism that diagnoses error types, identifies their causes, provides fixing instructions, and applies these corrections to SQL queries. This approach is further enriched by embedding fine-tuning and RAG, which harnesses external knowledge bases for improved accuracy and transparency. Through comprehensive experiments, we demonstrate that our framework achieves a significant 12 percent accuracy improvement over existing baselines, highlighting its potential to revolutionize data access and handling in contemporary data-driven environments.
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            <a href="https://www.alphaxiv.org/abs/2511.08145v1" target="_blank" rel="noopener noreferrer">
                仍未达成：大型语言模型能否在诗歌转散文任务上超越小型任务特定序列到序列模型？
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            Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kunal Kingkar Das, Manoj Balaji Jagadeeshan, Nallani Chakravartula Sahith, Jivne...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注诗歌到散文转换这一特定文本生成任务，属于纯粹的NLP应用领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及LLM与小型模型的比较，但这种内容生成任务没有明确的RecSys/Search/Ads应用场景，属于被排除的'纯粹LLM中心化主题'范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:56:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08145v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08145v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are increasingly treated as universal, general-purpose solutions across NLP tasks, particularly in English. But does this assumption hold for low-resource, morphologically rich languages such as Sanskrit? We address this question by comparing instruction-tuned and in-context-prompted LLMs with smaller task-specific encoder-decoder models on the Sanskrit poetry-to-prose conversion task. This task is intrinsically challenging: Sanskrit verse exhibits free word order combined with rigid metrical constraints, and its conversion to canonical prose (anvaya) requires multi-step reasoning involving compound segmentation, dependency resolution, and syntactic linearisation. This makes it an ideal testbed to evaluate whether LLMs can surpass specialised models. For LLMs, we apply instruction fine-tuning on general-purpose models and design in-context learning templates grounded in Paninian grammar and classical commentary heuristics. For task-specific modelling, we fully fine-tune a ByT5-Sanskrit Seq2Seq model. Our experiments show that domain-specific fine-tuning of ByT5-Sanskrit significantly outperforms all instruction-driven LLM approaches. Human evaluation strongly corroborates this result, with scores exhibiting high correlation with Kendall's Tau scores. Additionally, our prompting strategies provide an alternative to fine-tuning when domain-specific verse corpora are unavailable, and the task-specific Seq2Seq model demonstrates robust generalisation on out-of-domain evaluations.
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            <a href="https://www.alphaxiv.org/abs/2511.08226v1" target="_blank" rel="noopener noreferrer">
                在线补丁冗余消除器（OPRE）：一种基于数据集压缩的在线不可知持续学习新方法
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            The Online Patch Redundancy Eliminator (OPRE): A novel approach to online agnostic continual learning using dataset compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Raphaël Bayle, Martial Mermillod, Robert M. French
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注持续学习和数据集压缩技术，虽然这些技术可能间接应用于推荐系统的模型更新场景，但论文本身并未明确涉及推荐、搜索或广告领域的核心问题。论文标题中缺乏与Transformer架构、LLM技术或异构数据建模的直接关联，因此与当前关注点的相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:29:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08226v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08226v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    In order to achieve Continual Learning (CL), the problem of catastrophic forgetting, one that has plagued neural networks since their inception, must be overcome. The evaluation of continual learning methods relies on splitting a known homogeneous dataset and learning the associated tasks one after the other. We argue that most CL methods introduce a priori information about the data to come and cannot be considered agnostic. We exemplify this point with the case of methods relying on pretrained feature extractors, which are still used in CL. After showing that pretrained feature extractors imply a loss of generality with respect to the data that can be learned by the model, we then discuss other kinds of a priori information introduced in other CL methods. We then present the Online Patch Redundancy Eliminator (OPRE), an online dataset compression algorithm, which, along with the training of a classifier at test time, yields performance on CIFAR-10 and CIFAR-100 superior to a number of other state-of-the-art online continual learning methods. Additionally, OPRE requires only minimal and interpretable hypothesis on the data to come. We suggest that online dataset compression could well be necessary to achieve fully agnostic CL.
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            <a href="https://www.alphaxiv.org/abs/2511.08215v1" target="_blank" rel="noopener noreferrer">
                基于EfficientNet-B4视觉骨干网络评估Gemini大语言模型在食物图像食谱与营养描述任务中的表现
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            Evaluating Gemini LLM in Food Image-Based Recipe and Nutrition Description with EfficientNet-B4 Visual Backbone
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rizal Khoirul Anam
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注食物图像理解与描述生成，属于视觉语言模型在特定垂直领域的应用。虽然涉及LLM（Gemini）和视觉骨干网络，但其应用场景（食物图像食谱和营养描述）与搜索、推荐、广告的核心业务领域关联度较低，更偏向于医疗健康或特定垂直领域的应用，而非通用的RecSys/Search/Ads技术进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:17:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08215v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08215v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The proliferation of digital food applications necessitates robust methods for automated nutritional analysis and culinary guidance. This paper presents a comprehensive comparative evaluation of a decoupled, multimodal pipeline for food recognition. We evaluate a system integrating a specialized visual backbone (EfficientNet-B4) with a powerful generative large language model (Google's Gemini LLM). The core objective is to evaluate the trade-offs between visual classification accuracy, model efficiency, and the quality of generative output (nutritional data and recipes). We benchmark this pipeline against alternative vision backbones (VGG-16, ResNet-50, YOLOv8) and a lightweight LLM (Gemma). We introduce a formalization for "Semantic Error Propagation" (SEP) to analyze how classification inaccuracies from the visual module cascade into the generative output. Our analysis is grounded in a new Custom Chinese Food Dataset (CCFD) developed to address cultural bias in public datasets. Experimental results demonstrate that while EfficientNet-B4 (89.0\% Top-1 Acc.) provides the best balance of accuracy and efficiency, and Gemini (9.2/10 Factual Accuracy) provides superior generative quality, the system's overall utility is fundamentally bottlenecked by the visual front-end's perceptive accuracy. We conduct a detailed per-class analysis, identifying high semantic similarity as the most critical failure mode.
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            <a href="https://www.alphaxiv.org/abs/2511.08048v1" target="_blank" rel="noopener noreferrer">
                基于渐进式查询聚合的广义尺度目标计数
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Generalized-Scale Object Counting with Gradual Query Aggregation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jer Pelhan, Alan Lukezic, Matej Kristan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的目标计数任务，属于视觉领域的特定应用。虽然标题提到'广义尺度'和'查询聚合'技术，但这些概念与推荐系统、搜索或广告中的核心问题（如用户行为建模、内容排序、特征交互）缺乏直接关联。没有明确的机制表明这些视觉计数技术能够应用于异构数据处理或推荐系统架构。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:52:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08048v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08048v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Few-shot detection-based counters estimate the number of instances in the image specified only by a few test-time exemplars. A common approach to localize objects across multiple sizes is to merge backbone features of different resolutions. Furthermore, to enable small object detection in densely populated regions, the input image is commonly upsampled and tiling is applied to cope with the increased computational and memory requirements. Because of these ad-hoc solutions, existing counters struggle with images containing diverse-sized objects and densely populated regions of small objects. We propose GECO2, an end-to-end few-shot counting and detection method that explicitly addresses the object scale issues. A new dense query representation gradually aggregates exemplar-specific feature information across scales that leads to high-resolution dense queries that enable detection of large as well as small objects. GECO2 surpasses state-of-the-art few-shot counters in counting as well as detection accuracy by 10% while running 3x times faster at smaller GPU memory footprint.
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            <a href="https://www.alphaxiv.org/abs/2511.07976v1" target="_blank" rel="noopener noreferrer">
                穿越时间的形态变换：基于扩散模型的时序间隙桥接，用于变化检测中的鲁棒对齐
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            Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seyedehanita Madani, Vishal M. Patel
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的变化检测任务，涉及扩散模型在时序图像对齐中的应用。虽然扩散模型是生成式AI的重要技术，但该工作专注于纯粹的视觉变化检测问题，与推荐系统、搜索或广告中的核心排名、用户建模等任务没有直接关联，也没有展示在异构数据处理方面的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:40:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07976v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07976v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Remote sensing change detection is often challenged by spatial misalignment between bi-temporal images, especially when acquisitions are separated by long seasonal or multi-year gaps. While modern convolutional and transformer-based models perform well on aligned data, their reliance on precise co-registration limits their robustness in real-world conditions. Existing joint registration-detection frameworks typically require retraining and transfer poorly across domains. We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks. The framework integrates diffusion-based semantic morphing, dense registration, and residual flow refinement. A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences between consecutive frames. The composed flow is then refined through a lightweight U-Net to produce a high-fidelity warp that co-registers the original image pair. Extensive experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection across multiple backbones, demonstrating the generality and effectiveness of the proposed approach.
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            <a href="https://www.alphaxiv.org/abs/2511.07819v1" target="_blank" rel="noopener noreferrer">
                基于统一场景语义占用的3D场景中人体运动合成
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            Human Motion Synthesis in 3D Scenes via Unified Scene Semantic Occupancy
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gong Jingyu, Tong Kunkun, Chen Zhuoran, Yuan Chuanhan, Chen Mingang, Zhang Zhizh...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D场景中的人体运动合成，这属于计算机视觉和图形学领域，与推荐系统、搜索或广告的核心技术相关性较弱。虽然场景语义理解技术可能间接应用于某些用户行为建模场景，但缺乏明确的直接应用路径到RecSys/Search/Ads领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:33:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07819v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07819v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Human motion synthesis in 3D scenes relies heavily on scene comprehension, while current methods focus mainly on scene structure but ignore the semantic understanding. In this paper, we propose a human motion synthesis framework that take an unified Scene Semantic Occupancy (SSO) for scene representation, termed SSOMotion. We design a bi-directional tri-plane decomposition to derive a compact version of the SSO, and scene semantics are mapped to an unified feature space via CLIP encoding and shared linear dimensionality reduction. Such strategy can derive the fine-grained scene semantic structures while significantly reduce redundant computations. We further take these scene hints and movement direction derived from instructions for motion control via frame-wise scene query. Extensive experiments and ablation studies conducted on cluttered scenes using ShapeNet furniture, as well as scanned scenes from PROX and Replica datasets, demonstrate its cutting-edge performance while validating its effectiveness and generalization ability. Code will be publicly available at https://github.com/jingyugong/SSOMotion.
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            <a href="https://www.alphaxiv.org/abs/2511.07806v1" target="_blank" rel="noopener noreferrer">
                PC-Diffusion：通过偏好分类器将扩散模型与人类偏好对齐
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            PC-Diffusion: Aligning Diffusion Models with Human Preferences via Preference Classifier
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shaomeng Wang, He Wang, Xiaolu Wei, Longquan Dai, Jinhui Tang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型与人类偏好的对齐技术，属于AIGC和内容生成领域。虽然偏好学习在推荐系统中具有潜在应用，但该论文标题明确聚焦于扩散模型，属于被排除的'纯粹LLM中心化主题'和'AIGC、内容生成'范畴，与当前关注的推荐系统、搜索或广告的核心进展相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:53:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07806v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07806v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion models have achieved remarkable success in conditional image generation, yet their outputs often remain misaligned with human preferences. To address this, recent work has applied Direct Preference Optimization (DPO) to diffusion models, yielding significant improvements.~However, DPO-like methods exhibit two key limitations: 1) High computational cost,due to the entire model fine-tuning; 2) Sensitivity to reference model quality}, due to its tendency to introduce instability and bias. To overcome these limitations, we propose a novel framework for human preference alignment in diffusion models (PC-Diffusion), using a lightweight, trainable Preference Classifier that directly models the relative preference between samples. By restricting preference learning to this classifier, PC-Diffusion decouples preference alignment from the generative model, eliminating the need for entire model fine-tuning and reference model reliance.~We further provide theoretical guarantees for PC-Diffusion:1) PC-Diffusion ensures that the preference-guided distributions are consistently propagated across timesteps. 2)The training objective of the preference classifier is equivalent to DPO, but does not require a reference model.3) The proposed preference-guided correction can progressively steer generation toward preference-aligned regions.~Empirical results show that PC-Diffusion achieves comparable preference consistency to DPO while significantly reducing training costs and enabling efficient and stable preference-guided generation.
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            <a href="https://www.alphaxiv.org/abs/2511.07756v1" target="_blank" rel="noopener noreferrer">
                超越随机性：理解扩散模型中噪声的顺序
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Beyond Randomness: Understand the Order of the Noise in Diffusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Song Yan, Min Li, Bi Xinliang, Jian Yang, Yusen Zhang, Guanye Xiong, Yunwei Lan,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究扩散模型中的噪声特性，这属于生成式AI的底层技术，与推荐系统、搜索或广告的核心技术栈关联较弱。虽然扩散模型在内容生成方面有应用，但根据排除标准，纯粹的生成式AI技术（如AIGC、内容生成）被视为无关主题，除非有明确的推荐/搜索应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 02:12:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07756v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07756v1
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                    In text-driven content generation (T2C) diffusion model, semantic of generated content is mostly attributed to the process of text embedding and attention mechanism interaction. The initial noise of the generation process is typically characterized as a random element that contributes to the diversity of the generated content. Contrary to this view, this paper reveals that beneath the random surface of noise lies strong analyzable patterns. Specifically, this paper first conducts a comprehensive analysis of the impact of random noise on the model's generation. We found that noise not only contains rich semantic information, but also allows for the erasure of unwanted semantics from it in an extremely simple way based on information theory, and using the equivalence between the generation process of diffusion model and semantic injection to inject semantics into the cleaned noise. Then, we mathematically decipher these observations and propose a simple but efficient training-free and universal two-step "Semantic Erasure-Injection" process to modulate the initial noise in T2C diffusion model. Experimental results demonstrate that our method is consistently effective across various T2C models based on both DiT and UNet architectures and presents a novel perspective for optimizing the generation of diffusion model, providing a universal tool for consistent generation.
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            <a href="https://www.alphaxiv.org/abs/2511.07755v1" target="_blank" rel="noopener noreferrer">
                Filtered-ViT：针对多种对抗性补丁攻击的鲁棒防御
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            Filtered-ViT: A Robust Defense Against Multiple Adversarial Patch Attacks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aja Khanal, Ahmed Faid, Apurva Narayan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的对抗性攻击防御，属于纯粹的安全/防御性研究。虽然涉及Vision Transformer架构，但论文焦点是安全防御而非推荐系统、搜索或广告的核心技术进展。对抗性防御在推荐/搜索/广告领域的潜在应用非常有限且间接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 02:12:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07755v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07755v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Deep learning vision systems are increasingly deployed in safety-critical domains such as healthcare, yet they remain vulnerable to small adversarial patches that can trigger misclassifications. Most existing defenses assume a single patch and fail when multiple localized disruptions occur, the type of scenario adversaries and real-world artifacts often exploit. We propose Filtered-ViT, a new vision transformer architecture that integrates SMART Vector Median Filtering (SMART-VMF), a spatially adaptive, multi-scale, robustness-aware mechanism that enables selective suppression of corrupted regions while preserving semantic detail. On ImageNet with LaVAN multi-patch attacks, Filtered-ViT achieves 79.8% clean accuracy and 46.3% robust accuracy under four simultaneous 1\% patches, outperforming existing defenses. Beyond synthetic benchmarks, a real-world case study on radiographic medical imagery shows that Filtered-ViT mitigates natural artifacts such as occlusions and scanner noise without degrading diagnostic content. This establishes Filtered-ViT as the first transformer to demonstrate unified robustness against both adversarial and naturally occurring patch-like disruptions, charting a path toward reliable vision systems in truly high-stakes environments.
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            <a href="https://www.alphaxiv.org/abs/2511.08549v1" target="_blank" rel="noopener noreferrer">
                基于视觉Transformer的用户设备定位
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Vision Transformer Based User Equipment Positioning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Parshwa Shah, Dhaval K. Patel, Brijesh Soni, Miguel López-Benítez, Siddhartan Go...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然使用了Transformer架构，但主要关注计算机视觉中的定位任务，与推荐系统、搜索或广告的核心领域进展缺乏直接关联。视觉定位技术可能通过位置感知服务间接应用于广告，但这种应用过于间接且非核心。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:31:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08549v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08549v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.NI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, Deep Learning (DL) techniques have been used for User Equipment (UE) positioning. However, the key shortcomings of such models is that: i) they weigh the same attention to the entire input; ii) they are not well suited for the non-sequential data e.g., when only instantaneous Channel State Information (CSI) is available. In this context, we propose an attention-based Vision Transformer (ViT) architecture that focuses on the Angle Delay Profile (ADP) from CSI matrix. Our approach, validated on the `DeepMIMO' and `ViWi' ray-tracing datasets, achieves an Root Mean Squared Error (RMSE) of 0.55m indoors, 13.59m outdoors in DeepMIMO, and 3.45m in ViWi's outdoor blockage scenario. The proposed scheme outperforms state-of-the-art schemes by $\sim$ 38\%. It also performs substantially better than other approaches that we have considered in terms of the distribution of error distance.
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            <a href="https://www.alphaxiv.org/abs/2511.08512v1" target="_blank" rel="noopener noreferrer">
                CleverBirds：用于细粒度人类知识追踪的多项选择基准
            </a>
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        <div class="mb-2 text-base text-gray-700">
            CleverBirds: A Multiple-Choice Benchmark for Fine-grained Human Knowledge Tracing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Leonie Bossemeyer, Samuel Heinrich, Grant Van Horn, Oisin Mac Aodha
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注知识追踪基准测试，属于教育技术领域，与推荐系统、搜索或广告的核心技术关联度极低。虽然知识追踪可能涉及用户建模，但其特定的教育应用场景和基准测试性质使其难以直接应用于商业推荐、搜索或广告系统。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:51:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08512v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08512v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Mastering fine-grained visual recognition, essential in many expert domains, can require that specialists undergo years of dedicated training. Modeling the progression of such expertize in humans remains challenging, and accurately inferring a human learner's knowledge state is a key step toward understanding visual learning. We introduce CleverBirds, a large-scale knowledge tracing benchmark for fine-grained bird species recognition. Collected by the citizen-science platform eBird, it offers insight into how individuals acquire expertize in complex fine-grained classification. More than 40,000 participants have engaged in the quiz, answering over 17 million multiple-choice questions spanning over 10,000 bird species, with long-range learning patterns across an average of 400 questions per participant. We release this dataset to support the development and evaluation of new methods for visual knowledge tracing. We show that tracking learners' knowledge is challenging, especially across participant subgroups and question types, with different forms of contextual information offering varying degrees of predictive benefit. CleverBirds is among the largest benchmark of its kind, offering a substantially higher number of learnable concepts. With it, we hope to enable new avenues for studying the development of visual expertize over time and across individuals.
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            <a href="https://www.alphaxiv.org/abs/2511.07948v1" target="_blank" rel="noopener noreferrer">
                ReIDMamba：基于视觉状态空间模型学习判别性特征的行人重识别
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        <div class="mb-2 text-base text-gray-700">
            ReIDMamba: Learning Discriminative Features with Visual State Space Model for Person Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongyang Gu, Qisong Yang, Lei Pu, Siming Han, Yao Ding
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的行人重识别任务，虽然涉及状态空间模型架构，但其核心应用场景是视觉识别而非推荐系统、搜索或广告。视觉状态空间模型可能具有架构效率优势，但论文标题明确限定于视觉特征学习和行人重识别这一特定视觉任务，与当前关注的推荐系统、搜索广告核心领域缺乏直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:01:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07948v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07948v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Extracting robust discriminative features is a critical challenge in person re-identification (ReID). While Transformer-based methods have successfully addressed some limitations of convolutional neural networks (CNNs), such as their local processing nature and information loss resulting from convolution and downsampling operations, they still face the scalability issue due to the quadratic increase in memory and computational requirements with the length of the input sequence. To overcome this, we propose a pure Mamba-based person ReID framework named ReIDMamba. Specifically, we have designed a Mamba-based strong baseline that effectively leverages fine-grained, discriminative global features by introducing multiple class tokens. To further enhance robust features learning within Mamba, we have carefully designed two novel techniques. First, the multi-granularity feature extractor (MGFE) module, designed with a multi-branch architecture and class token fusion, effectively forms multi-granularity features, enhancing both discrimination ability and fine-grained coverage. Second, the ranking-aware triplet regularization (RATR) is introduced to reduce redundancy in features from multiple branches, enhancing the diversity of multi-granularity features by incorporating both intra-class and inter-class diversity constraints, thus ensuring the robustness of person features. To our knowledge, this is the pioneering work that integrates a purely Mamba-driven approach into ReID research. Our proposed ReIDMamba model boasts only one-third the parameters of TransReID, along with lower GPU memory usage and faster inference throughput. Experimental results demonstrate ReIDMamba's superior and promising performance, achieving state-of-the-art performance on five person ReID benchmarks. Code is available at https://github.com/GuHY777/ReIDMamba.
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            <a href="https://www.alphaxiv.org/abs/2511.07935v1" target="_blank" rel="noopener noreferrer">
                DiffRegCD：基于扩散特征的集成配准与变化检测
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            DiffRegCD: Integrated Registration and Change Detection with Diffusion Features
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seyedehnanita Madani, Rama Chellappa, Vishal M. Patel
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的图像配准和变化检测任务，使用扩散模型特征。虽然扩散模型是生成模型的一种，但该工作聚焦于纯粹的视觉分析应用，没有展示与推荐系统、搜索或广告的明显联系。其技术方法缺乏对用户行为建模、内容排序或个性化推荐等核心任务的直接适用性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:32:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07935v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07935v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Change detection (CD) is fundamental to computer vision and remote sensing, supporting applications in environmental monitoring, disaster response, and urban development. Most CD models assume co-registered inputs, yet real-world imagery often exhibits parallax, viewpoint shifts, and long temporal gaps that cause severe misalignment. Traditional two stage methods that first register and then detect, as well as recent joint frameworks (e.g., BiFA, ChangeRD), still struggle under large displacements, relying on regression only flow, global homographies, or synthetic perturbations. We present DiffRegCD, an integrated framework that unifies dense registration and change detection in a single model. DiffRegCD reformulates correspondence estimation as a Gaussian smoothed classification task, achieving sub-pixel accuracy and stable training. It leverages frozen multi-scale features from a pretrained denoising diffusion model, ensuring robustness to illumination and viewpoint variation. Supervision is provided through controlled affine perturbations applied to standard CD datasets, yielding paired ground truth for both flow and change detection without pseudo labels. Extensive experiments on aerial (LEVIR-CD, DSIFN-CD, WHU-CD, SYSU-CD) and ground level (VL-CMU-CD) datasets show that DiffRegCD consistently surpasses recent baselines and remains reliable under wide temporal and geometric variation, establishing diffusion features and classification based correspondence as a strong foundation for unified change detection.
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            <a href="https://www.alphaxiv.org/abs/2511.07813v1" target="_blank" rel="noopener noreferrer">
                Sparse3DPR：基于稀疏RGB视图的免训练3D层次化场景解析与任务自适应子图推理
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Sparse3DPR: Training-Free 3D Hierarchical Scene Parsing and Task-Adaptive Subgraph Reasoning from Sparse RGB Views
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haida Feng, Hao Wei, Zewen Xu, Haolin Wang, Chade Li, Yihong Wu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D视觉场景解析和子图推理，属于计算机视觉领域。虽然提到了层次化解析和推理技术，但这些方法与推荐系统、搜索或广告的核心技术（如排序、召回、用户建模）没有直接关联。3D场景理解在电商产品展示或AR购物中可能有间接应用，但论文本身并未明确指向这些领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:13:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07813v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07813v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recently, large language models (LLMs) have been explored widely for 3D scene understanding. Among them, training-free approaches are gaining attention for their flexibility and generalization over training-based methods. However, they typically struggle with accuracy and efficiency in practical deployment. To address the problems, we propose Sparse3DPR, a novel training-free framework for open-ended scene understanding, which leverages the reasoning capabilities of pre-trained LLMs and requires only sparse-view RGB inputs. Specifically, we introduce a hierarchical plane-enhanced scene graph that supports open vocabulary and adopts dominant planar structures as spatial anchors, which enables clearer reasoning chains and more reliable high-level inferences. Furthermore, we design a task-adaptive subgraph extraction method to filter query-irrelevant information dynamically, reducing contextual noise and improving 3D scene reasoning efficiency and accuracy. Experimental results demonstrate the superiority of Sparse3DPR, which achieves a 28.7% EM@1 improvement and a 78.2% speedup compared with ConceptGraphs on the Space3D-Bench. Moreover, Sparse3DPR obtains comparable performance to training-based methods on ScanQA, with additional real-world experiments confirming its robustness and generalization capability.
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            <a href="https://www.alphaxiv.org/abs/2511.07812v1" target="_blank" rel="noopener noreferrer">
                重新审视基于多模态大语言模型的图像质量评估：错误与补救
            </a>
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            Revisiting MLLM Based Image Quality Assessment: Errors and Remedy
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenchen Tang, Songlin Yang, Bo Peng, Zichuan Wang, Jing Dong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于多模态大语言模型在图像质量评估领域的应用，这属于纯粹的视觉质量评估任务，与推荐系统、搜索或广告的排序和相关性建模没有直接关联。虽然涉及多模态模型，但应用场景是图像质量评估这一特定视觉任务，无法为RecSys/Search/Ads领域提供直接的技术借鉴或应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:08:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07812v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07812v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch between the discrete token outputs of MLLMs and the continuous nature of quality scores required by IQA tasks. This discrepancy significantly hinders the performance of MLLM-based IQA methods. Previous approaches that convert discrete token predictions into continuous scores often suffer from conversion errors. Moreover, the semantic confusion introduced by level tokens (e.g., ``good'') further constrains the performance of MLLMs on IQA tasks and degrades their original capabilities for related tasks. To tackle these problems, we provide a theoretical analysis of the errors inherent in previous approaches and, motivated by this analysis, propose a simple yet effective framework, Q-Scorer. This framework incorporates a lightweight regression module and IQA-specific score tokens into the MLLM pipeline. Extensive experiments demonstrate that Q-Scorer achieves state-of-the-art performance across multiple IQA benchmarks, generalizes well to mixed datasets, and further improves when combined with other methods.
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            <a href="https://www.alphaxiv.org/abs/2511.07798v1" target="_blank" rel="noopener noreferrer">
                用于跨域少样本分割的分治解耦网络
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            <i class="fa fa-star mr-1"></i>2/10
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            Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Runmin Cong, Anpeng Wang, Bin Wan, Cong Zhang, Xiaofei Zhou, Wei Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的少样本分割任务，虽然涉及跨域学习和高效架构设计，但与推荐系统、搜索或广告的核心技术关联度较低。分割技术可能在广告创意分析中有边缘应用，但缺乏明确的RecSys/Search/Ads应用场景，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:29:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07798v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07798v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder features often entangle domain-relevant and category-relevant information, limiting both generalization and rapid adaptation to new domains. To address this issue, we propose a Divide-and-Conquer Decoupled Network (DCDNet). In the training stage, to tackle feature entanglement that impedes cross-domain generalization and rapid adaptation, we propose the Adversarial-Contrastive Feature Decomposition (ACFD) module. It decouples backbone features into category-relevant private and domain-relevant shared representations via contrastive learning and adversarial learning. Then, to mitigate the potential degradation caused by the disentanglement, the Matrix-Guided Dynamic Fusion (MGDF) module adaptively integrates base, shared, and private features under spatial guidance, maintaining structural coherence. In addition, in the fine-tuning stage, to enhanced model generalization, the Cross-Adaptive Modulation (CAM) module is placed before the MGDF, where shared features guide private features via modulation ensuring effective integration of domain-relevant information. Extensive experiments on four challenging datasets show that DCDNet outperforms existing CD-FSS methods, setting a new state-of-the-art for cross-domain generalization and few-shot adaptation.
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            <a href="https://www.alphaxiv.org/abs/2511.07744v1" target="_blank" rel="noopener noreferrer">
                VectorSynth：基于结构化语义的细粒度卫星图像合成
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            VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniel Cher, Brian Wei, Srikumar Sastry, Nathan Jacobs
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于卫星图像合成技术，属于纯粹的视觉生成领域。虽然标题提到结构化语义，但这主要针对地理空间数据的表示，与推荐系统、搜索或广告中的用户行为序列、上下文特征等异构数据处理没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明确应用路径。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 01:54:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07744v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07744v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We introduce VectorSynth, a diffusion-based framework for pixel-accurate satellite image synthesis conditioned on polygonal geographic annotations with semantic attributes. Unlike prior text- or layout-conditioned models, VectorSynth learns dense cross-modal correspondences that align imagery and semantic vector geometry, enabling fine-grained, spatially grounded edits. A vision language alignment module produces pixel-level embeddings from polygon semantics; these embeddings guide a conditional image generation framework to respect both spatial extents and semantic cues. VectorSynth supports interactive workflows that mix language prompts with geometry-aware conditioning, allowing rapid what-if simulations, spatial edits, and map-informed content generation. For training and evaluation, we assemble a collection of satellite scenes paired with pixel-registered polygon annotations spanning diverse urban scenes with both built and natural features. We observe strong improvements over prior methods in semantic fidelity and structural realism, and show that our trained vision language model demonstrates fine-grained spatial grounding. The code and data are available at https://github.com/mvrl/VectorSynth.
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            <a href="https://www.alphaxiv.org/abs/2511.08423v1" target="_blank" rel="noopener noreferrer">
                OmniAID：解耦语义与伪影以实现通用AI生成图像检测
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            OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated Image Detection in the Wild
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuncheng Guo, Junyan Ye, Chenjue Zhang, Hengrui Kang, Haohuan Fu, Conghui He, We...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于AI生成图像检测技术，属于内容真实性验证领域。虽然检测技术可能间接应用于广告或推荐系统中的内容审核，但这与我的核心关注点（推荐系统、搜索、广告的排序算法、LLM应用、Transformer架构进展）关联度较低，且更偏向内容安全而非核心排序技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:33:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08423v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08423v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    A truly universal AI-Generated Image (AIGI) detector must simultaneously generalize across diverse generative models and varied semantic content. Current state-of-the-art methods learn a single, entangled forgery representation--conflating content-dependent flaws with content-agnostic artifacts--and are further constrained by outdated benchmarks. To overcome these limitations, we propose OmniAID, a novel framework centered on a decoupled Mixture-of-Experts (MoE) architecture. The core of our method is a hybrid expert system engineered to decouple: (1) semantic flaws across distinct content domains, and (2) these content-dependent flaws from content-agnostic universal artifacts. This system employs a set of Routable Specialized Semantic Experts, each for a distinct domain (e.g., human, animal), complemented by a Fixed Universal Artifact Expert. This architecture is trained using a bespoke two-stage strategy: we first train the experts independently with domain-specific hard-sampling to ensure specialization, and subsequently train a lightweight gating network for effective input routing. By explicitly decoupling "what is generated" (content-specific flaws) from "how it is generated" (universal artifacts), OmniAID achieves robust generalization. To address outdated benchmarks and validate real-world applicability, we introduce Mirage, a new large-scale, contemporary dataset. Extensive experiments, using both traditional benchmarks and our Mirage dataset, demonstrate our model surpasses existing monolithic detectors, establishing a new, robust standard for AIGI authentication against modern, in-the-wild threats.
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            <a href="https://www.alphaxiv.org/abs/2511.08009v1" target="_blank" rel="noopener noreferrer">
                从噪声到潜在：为基于隐式神经表示的图像压缩生成高斯潜在变量
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chaoyi Lin, Yaojun Wu, Yue Li, Junru Li, Kai Zhang, Li Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图像压缩中的隐式神经表示（INR）技术，属于纯粹的计算机视觉领域。虽然压缩技术可能间接影响多媒体内容的存储和传输，但论文没有展示与推荐系统、搜索或广告中核心排名、用户建模或内容理解任务的直接关联。其技术路径专注于视觉数据压缩，而非处理推荐/搜索场景中的异构数据或多模态建模。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:12:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08009v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08009v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent implicit neural representation (INR)-based image compression methods have shown competitive performance by overfitting image-specific latent codes. However, they remain inferior to end-to-end (E2E) compression approaches due to the absence of expressive latent representations. On the other hand, E2E methods rely on transmitting latent codes and requiring complex entropy models, leading to increased decoding complexity. Inspired by the normalization strategy in E2E codecs where latents are transformed into Gaussian noise to demonstrate the removal of spatial redundancy, we explore the inverse direction: generating latents directly from Gaussian noise. In this paper, we propose a novel image compression paradigm that reconstructs image-specific latents from a multi-scale Gaussian noise tensor, deterministically generated using a shared random seed. A Gaussian Parameter Prediction (GPP) module estimates the distribution parameters, enabling one-shot latent generation via reparameterization trick. The predicted latent is then passed through a synthesis network to reconstruct the image. Our method eliminates the need to transmit latent codes while preserving latent-based benefits, achieving competitive rate-distortion performance on Kodak and CLIC dataset. To the best of our knowledge, this is the first work to explore Gaussian latent generation for learned image compression.
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            <a href="https://www.alphaxiv.org/abs/2511.07930v1" target="_blank" rel="noopener noreferrer">
                IBMA：一种基于自监督学习的时间序列数据插补混合增强方法
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            IBMA: An Imputation-Based Mixup Augmentation Using Self-Supervised Learning for Time Series Data
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dang Nha Nguyen, Hai Dang Nguyen, Khoa Tho Anh Nguyen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注时间序列数据的增强技术，虽然自监督学习和数据增强在推荐系统中可能有间接应用，但论文标题明确聚焦于时间序列数据而非推荐/搜索/广告的核心问题。没有明确证据表明该方法专门针对RecSys/Search/Ads场景，且不属于核心领域进展或直接LLM应用范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:27:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07930v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07930v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Data augmentation in time series forecasting plays a crucial role in enhancing model performance by introducing variability while maintaining the underlying temporal patterns. However, time series data offers fewer augmentation strategies compared to fields such as image or text, with advanced techniques like Mixup rarely being used. In this work, we propose a novel approach, Imputation-Based Mixup Augmentation (IBMA), which combines Imputation-Augmented data with Mixup augmentation to bolster model generalization and improve forecasting performance. We evaluate the effectiveness of this method across several forecasting models, including DLinear (MLP), TimesNet (CNN), and iTrainformer (Transformer), these models represent some of the most recent advances in time series forecasting. Our experiments, conducted on four datasets (ETTh1, ETTh2, ETTm1, ETTm2) and compared against eight other augmentation techniques, demonstrate that IBMA consistently enhances performance, achieving 22 improvements out of 24 instances, with 10 of those being the best performances, particularly with iTrainformer imputation.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07903v1" target="_blank" rel="noopener noreferrer">
                DynaQuant：用于学习型图像压缩的动态混合精度量化
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            DynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Youneng Bao, Yulong Cheng, Yiping Liu, Yichen Yang, Peng Qin, Mu Li, Yongsheng L...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像压缩领域的量化技术，虽然量化是模型效率的重要技术，但其应用场景明确限定在图像压缩领域，与推荐系统、搜索或广告的核心技术栈关联性较弱。论文没有展示在序列建模、特征交互或多模态学习方面的潜在应用价值，因此对当前关注领域的直接相关性较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:58:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07903v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07903v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Prevailing quantization techniques in Learned Image Compression (LIC) typically employ a static, uniform bit-width across all layers, failing to adapt to the highly diverse data distributions and sensitivity characteristics inherent in LIC models. This leads to a suboptimal trade-off between performance and efficiency. In this paper, we introduce DynaQuant, a novel framework for dynamic mixed-precision quantization that operates on two complementary levels. First, we propose content-aware quantization, where learnable scaling and offset parameters dynamically adapt to the statistical variations of latent features. This fine-grained adaptation is trained end-to-end using a novel Distance-aware Gradient Modulator (DGM), which provides a more informative learning signal than the standard Straight-Through Estimator. Second, we introduce a data-driven, dynamic bit-width selector that learns to assign an optimal bit precision to each layer, dynamically reconfiguring the network's precision profile based on the input data. Our fully dynamic approach offers substantial flexibility in balancing rate-distortion (R-D) performance and computational cost. Experiments demonstrate that DynaQuant achieves rd performance comparable to full-precision models while significantly reducing computational and storage requirements, thereby enabling the practical deployment of advanced LIC on diverse hardware platforms.
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            <a href="https://www.alphaxiv.org/abs/2511.08163v1" target="_blank" rel="noopener noreferrer">
                用于零样本学习的多粒度互精化网络
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Multi-Granularity Mutual Refinement Network for Zero-Shot Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ning Wang, Long Yu, Cong Hua, Guangming Zhu, Lin Mei, Syed Afaq Ali Shah, Mohamm...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注零样本学习，这是一个计算机视觉领域的技术，用于识别训练时未见过的类别。虽然多粒度表示和互精化机制在某些推荐系统场景中可能有潜在应用，但该论文的核心焦点是视觉零样本学习，与推荐系统、搜索或广告的直接关联性较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:23:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08163v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08163v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Zero-shot learning (ZSL) aims to recognize unseen classes with zero samples by transferring semantic knowledge from seen classes. Current approaches typically correlate global visual features with semantic information (i.e., attributes) or align local visual region features with corresponding attributes to enhance visual-semantic interactions. Although effective, these methods often overlook the intrinsic interactions between local region features, which can further improve the acquisition of transferable and explicit visual features. In this paper, we propose a network named Multi-Granularity Mutual Refinement Network (Mg-MRN), which refine discriminative and transferable visual features by learning decoupled multi-granularity features and cross-granularity feature interactions. Specifically, we design a multi-granularity feature extraction module to learn region-level discriminative features through decoupled region feature mining. Then, a cross-granularity feature fusion module strengthens the inherent interactions between region features of varying granularities. This module enhances the discriminability of representations at each granularity level by integrating region representations from adjacent hierarchies, further improving ZSL recognition performance. Extensive experiments on three popular ZSL benchmark datasets demonstrate the superiority and competitiveness of our proposed Mg-MRN method. Our code is available at https://github.com/NingWang2049/Mg-MRN.
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            <a href="https://www.alphaxiv.org/abs/2511.08348v1" target="_blank" rel="noopener noreferrer">
                VideoChain：一种基于Transformer的多跳视频问答生成框架
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arpan Phukan, Anupam Pandey, Deepjyoti Bodo, Asif Ekbal
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频问答生成，属于视觉语言模型在视频领域的应用。虽然涉及Transformer架构，但其核心是视频内容理解和问答生成，与推荐系统、搜索或广告的排名和匹配任务关联度较低。多跳推理机制在推荐系统中可能有潜在应用，但论文标题未表明这种跨领域适用性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:26:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08348v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08348v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-hop Question Generation (QG) effectively evaluates reasoning but remains confined to text; Video Question Generation (VideoQG) is limited to zero-hop questions over single segments. To address this, we introduce VideoChain, a novel Multi-hop Video Question Generation (MVQG) framework designed to generate questions that require reasoning across multiple, temporally separated video segments. VideoChain features a modular architecture built on a modified BART backbone enhanced with video embeddings, capturing textual and visual dependencies. Using the TVQA+ dataset, we automatically construct the large-scale MVQ-60 dataset by merging zero-hop QA pairs, ensuring scalability and diversity. Evaluations show VideoChain's strong performance across standard generation metrics: ROUGE-L (0.6454), ROUGE-1 (0.6854), BLEU-1 (0.6711), BERTScore-F1 (0.7967), and semantic similarity (0.8110). These results highlight the model's ability to generate coherent, contextually grounded, and reasoning-intensive questions.
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            <a href="https://www.alphaxiv.org/abs/2511.08310v1" target="_blank" rel="noopener noreferrer">
                NeuSpring：基于神经弹簧场的可变形物体视频重建与仿真
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        <div class="mb-2 text-base text-gray-700">
            NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qingshan Xu, Jiao Liu, Shangshu Yu, Yuxuan Wang, Yuan Zhou, Junbao Zhou, Jiequan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的可变形物体重建与物理仿真，属于3D视觉和图形学领域。虽然神经场技术在概念上与某些推荐系统中的嵌入表示有相似之处，但该工作的具体应用场景（可变形物体重建与仿真）与搜索推荐广告系统的核心需求（用户行为建模、内容理解、排序优化）没有直接关联，且论文标题未显示出任何潜在的跨领域应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:40:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08310v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08310v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but generalize worse to future prediction. This is because existing methods ignore the intrinsic physical properties of deformable objects, resulting in the limited physical learning in the current state modeling. To address this, we present NeuSpring, a neural spring field for the reconstruction and simulation of deformable objects from videos. Built upon spring-mass models for realistic physical simulation, our method consists of two major innovations: 1) a piecewise topology solution that efficiently models multi-region spring connection topologies using zero-order optimization, which considers the material heterogeneity of real-world objects. 2) a neural spring field that represents spring physical properties across different frames using a canonical coordinate-based neural network, which effectively leverages the spatial associativity of springs for physical learning. Experiments on real-world datasets demonstrate that our NeuSping achieves superior reconstruction and simulation performance for current state modeling and future prediction, with Chamfer distance improved by 20% and 25%, respectively.
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            <a href="https://www.alphaxiv.org/abs/2511.08195v1" target="_blank" rel="noopener noreferrer">
                UI2Code$^\text{N}$：一种用于测试时可扩展交互式UI到代码生成的视觉语言模型
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            UI2Code$^\text{N}$: A Visual Language Model for Test-Time Scalable Interactive UI-to-Code Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhen Yang, Wenyi Hong, Mingde Xu, Xinyue Fan, Weihan Wang, Jiele Cheng, Xiaotao ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注UI到代码的视觉语言模型应用，属于计算机视觉和代码生成的交叉领域。虽然涉及视觉语言模型技术，但其应用场景（UI代码生成）与推荐系统、搜索或广告的核心技术需求关联度较低，没有明显的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:00:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08195v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08195v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    User interface (UI) programming is a core yet highly complex part of modern software development. Recent advances in visual language models (VLMs) highlight the potential of automatic UI coding, but current approaches face two key limitations: multimodal coding capabilities remain underdeveloped, and single-turn paradigms make little use of iterative visual feedback. We address these challenges with an interactive UI-to-code paradigm that better reflects real-world workflows and raises the upper bound of achievable performance. Under this paradigm, we present UI2Code$^\text{N}$, a visual language model trained through staged pretraining, fine-tuning, and reinforcement learning to achieve foundational improvements in multimodal coding. The model unifies three key capabilities: UI-to-code generation, UI editing, and UI polishing. We further explore test-time scaling for interactive generation, enabling systematic use of multi-turn feedback. Experiments on UI-to-code and UI polishing benchmarks show that UI2Code$^\text{N}$ establishes a new state of the art among open-source models and achieves performance comparable to leading closed-source models such as Claude-4-Sonnet and GPT-5. Our code and models are available at https://github.com/zai-org/UI2Code_N.
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            <a href="https://www.alphaxiv.org/abs/2511.08186v1" target="_blank" rel="noopener noreferrer">
                面向定向目标检测的像素级质量评估
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Pixel-level Quality Assessment for Oriented Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunhui Zhu, Buliao Huang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的定向目标检测和像素级质量评估，属于纯粹的视觉技术范畴。虽然目标检测在广告创意分析中有潜在应用，但论文标题没有表明与推荐系统、搜索或广告排名的直接相关性，也没有涉及LLM或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:54:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08186v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08186v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Modern oriented object detectors typically predict a set of bounding boxes and select the top-ranked ones based on estimated localization quality. Achieving high detection performance requires that the estimated quality closely aligns with the actual localization accuracy. To this end, existing approaches predict the Intersection over Union (IoU) between the predicted and ground-truth (GT) boxes as a proxy for localization quality. However, box-level IoU prediction suffers from a structural coupling issue: since the predicted box is derived from the detector's internal estimation of the GT box, the predicted IoU--based on their similarity--can be overestimated for poorly localized boxes. To overcome this limitation, we propose a novel Pixel-level Quality Assessment (PQA) framework, which replaces box-level IoU prediction with the integration of pixel-level spatial consistency. PQA measures the alignment between each pixel's relative position to the predicted box and its corresponding position to the GT box. By operating at the pixel level, PQA avoids directly comparing the predicted box with the estimated GT box, thereby eliminating the inherent similarity bias in box-level IoU prediction. Furthermore, we introduce a new integration metric that aggregates pixel-level spatial consistency into a unified quality score, yielding a more accurate approximation of the actual localization quality. Extensive experiments on HRSC2016 and DOTA demonstrate that PQA can be seamlessly integrated into various oriented object detectors, consistently improving performance (e.g., +5.96% AP$_{50:95}$ on Rotated RetinaNet and +2.32% on STD).
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            <a href="https://www.alphaxiv.org/abs/2511.08178v1" target="_blank" rel="noopener noreferrer">
                WarpGAN：基于形变引导的3D GAN反演与风格化新视角修复
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            <i class="fa fa-star mr-1"></i>2/10
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            WarpGAN: Warping-Guided 3D GAN Inversion with Style-Based Novel View Inpainting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaitao Huang, Yan Yan, Jing-Hao Xue, Hanzi Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D生成对抗网络和视角合成技术，属于计算机视觉和图形学领域。虽然标题提及GAN和风格化处理，但其核心应用方向（3D重建、新视角生成）与推荐系统、搜索或广告的排序和建模需求关联度极低。没有明确的机制或应用场景表明该技术能直接或间接服务于RecSys/Search/Ads的核心任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:42:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08178v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08178v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D GAN inversion projects a single image into the latent space of a pre-trained 3D GAN to achieve single-shot novel view synthesis, which requires visible regions with high fidelity and occluded regions with realism and multi-view consistency. However, existing methods focus on the reconstruction of visible regions, while the generation of occluded regions relies only on the generative prior of 3D GAN. As a result, the generated occluded regions often exhibit poor quality due to the information loss caused by the low bit-rate latent code. To address this, we introduce the warping-and-inpainting strategy to incorporate image inpainting into 3D GAN inversion and propose a novel 3D GAN inversion method, WarpGAN. Specifically, we first employ a 3D GAN inversion encoder to project the single-view image into a latent code that serves as the input to 3D GAN. Then, we perform warping to a novel view using the depth map generated by 3D GAN. Finally, we develop a novel SVINet, which leverages the symmetry prior and multi-view image correspondence w.r.t. the same latent code to perform inpainting of occluded regions in the warped image. Quantitative and qualitative experiments demonstrate that our method consistently outperforms several state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.08133v1" target="_blank" rel="noopener noreferrer">
                OTSNet：一种神经认知启发的观察-思考-拼写流程用于场景文本识别
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        <div class="mb-2 text-base text-gray-700">
            OTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lixu Sun, Nurmemet Yolwas, Wushour Silamu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于场景文本识别，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然文本识别技术可能在广告创意分析中有边缘应用，但这超出了当前对排名、推荐和搜索核心技术的关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:40:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08133v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08133v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Scene Text Recognition (STR) remains challenging due to real-world complexities, where decoupled visual-linguistic optimization in existing frameworks amplifies error propagation through cross-modal misalignment. Visual encoders exhibit attention bias toward background distractors, while decoders suffer from spatial misalignment when parsing geometrically deformed text-collectively degrading recognition accuracy for irregular patterns. Inspired by the hierarchical cognitive processes in human visual perception, we propose OTSNet, a novel three-stage network embodying a neurocognitive-inspired Observation-Thinking-Spelling pipeline for unified STR modeling. The architecture comprises three core components: (1) a Dual Attention Macaron Encoder (DAME) that refines visual features through differential attention maps to suppress irrelevant regions and enhance discriminative focus; (2) a Position-Aware Module (PAM) and Semantic Quantizer (SQ) that jointly integrate spatial context with glyph-level semantic abstraction via adaptive sampling; and (3) a Multi-Modal Collaborative Verifier (MMCV) that enforces self-correction through cross-modal fusion of visual, semantic, and character-level features. Extensive experiments demonstrate that OTSNet achieves state-of-the-art performance, attaining 83.5% average accuracy on the challenging Union14M-L benchmark and 79.1% on the heavily occluded OST dataset-establishing new records across 9 out of 14 evaluation scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.08032v1" target="_blank" rel="noopener noreferrer">
                3D高斯泼溅的感知质量评估：主观数据集与预测指标
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        <div class="mb-2 text-base text-gray-700">
            Perceptual Quality Assessment of 3D Gaussian Splatting: A Subjective Dataset and Prediction Metric
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhaolin Wan, Yining Diao, Jingqi Xu, Hao Wang, Zhiyang Li, Xiaopeng Fan, Wangmen...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉内容的感知质量评估，属于纯粹的计算机视觉领域研究。虽然3D高斯泼溅是一种新兴的3D表示技术，但论文主要关注视觉质量评估而非其在推荐、搜索或广告系统中的应用。没有明确的连接表明该技术能够直接应用于异构数据处理或Transformer架构改进。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:34:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08032v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08032v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    With the rapid advancement of 3D visualization, 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time, high-fidelity rendering. While prior research has emphasized algorithmic performance and visual fidelity, the perceptual quality of 3DGS-rendered content, especially under varying reconstruction conditions, remains largely underexplored. In practice, factors such as viewpoint sparsity, limited training iterations, point downsampling, noise, and color distortions can significantly degrade visual quality, yet their perceptual impact has not been systematically studied. To bridge this gap, we present 3DGS-QA, the first subjective quality assessment dataset for 3DGS. It comprises 225 degraded reconstructions across 15 object types, enabling a controlled investigation of common distortion factors. Based on this dataset, we introduce a no-reference quality prediction model that directly operates on native 3D Gaussian primitives, without requiring rendered images or ground-truth references. Our model extracts spatial and photometric cues from the Gaussian representation to estimate perceived quality in a structure-aware manner. We further benchmark existing quality assessment methods, spanning both traditional and learning-based approaches. Experimental results show that our method consistently achieves superior performance, highlighting its robustness and effectiveness for 3DGS content evaluation. The dataset and code are made publicly available at https://github.com/diaoyn/3DGSQA to facilitate future research in 3DGS quality assessment.
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            <a href="https://www.alphaxiv.org/abs/2511.08322v1" target="_blank" rel="noopener noreferrer">
                通过边界保持训练缓解负翻转
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            Mitigating Negative Flips via Margin Preserving Training
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Simone Ricci, Niccolò Biondi, Federico Pernici, Alberto Del Bimbo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题暗示了模型训练中的稳定性问题，可能涉及分类边界优化，但未明确与推荐系统、搜索或广告的核心技术相关。边界保持训练可能对模型鲁棒性有一般性改进，但缺乏明确的连接点表明在推荐/搜索/广告领域的直接应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:53:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08322v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08322v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Minimizing inconsistencies across successive versions of an AI system is as crucial as reducing the overall error. In image classification, such inconsistencies manifest as negative flips, where an updated model misclassifies test samples that were previously classified correctly. This issue becomes increasingly pronounced as the number of training classes grows over time, since adding new categories reduces the margin of each class and may introduce conflicting patterns that undermine their learning process, thereby degrading performance on the original subset. To mitigate negative flips, we propose a novel approach that preserves the margins of the original model while learning an improved one. Our method encourages a larger relative margin between the previously learned and newly introduced classes by introducing an explicit margin-calibration term on the logits. However, overly constraining the logit margin for the new classes can significantly degrade their accuracy compared to a new independently trained model. To address this, we integrate a double-source focal distillation loss with the previous model and a new independently trained model, learning an appropriate decision margin from both old and new data, even under a logit margin calibration. Extensive experiments on image classification benchmarks demonstrate that our approach consistently reduces the negative flip rate with high overall accuracy.
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            <a href="https://www.alphaxiv.org/abs/2511.07958v1" target="_blank" rel="noopener noreferrer">
                突发图像质量评估：面向多个下游任务的新基准与统一框架
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            <i class="fa fa-star mr-1"></i>2/10
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            Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoye Liang, Lai Jiang, Minglang Qiao, Yichen Guo, Yue Zhang, Xin Deng, Shengxi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像质量评估这一计算机视觉领域，与推荐系统、搜索或广告的核心技术关联度较低。虽然图像质量在电商搜索等场景中可能作为辅助特征，但该研究主要针对图像处理下游任务，缺乏明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:15:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07958v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07958v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, the redundancy in burst images leads to the increased storage and transmission demands, as well as reduced efficiency of downstream tasks. To address this, we propose a new task of Burst Image Quality Assessment (BuIQA), to evaluate the task-driven quality of each frame within a burst sequence, providing reasonable cues for burst image selection. Specifically, we establish the first benchmark dataset for BuIQA, consisting of $7,346$ burst sequences with $45,827$ images and $191,572$ annotated quality scores for multiple downstream scenarios. Inspired by the data analysis, a unified BuIQA framework is proposed to achieve an efficient adaption for BuIQA under diverse downstream scenarios. Specifically, a task-driven prompt generation network is developed with heterogeneous knowledge distillation, to learn the priors of the downstream task. Then, the task-aware quality assessment network is introduced to assess the burst image quality based on the task prompt. Extensive experiments across 10 downstream scenarios demonstrate the impressive BuIQA performance of the proposed approach, outperforming the state-of-the-art. Furthermore, it can achieve $0.33$ dB PSNR improvement in the downstream tasks of denoising and super-resolution, by applying our approach to select the high-quality burst frames.
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            <a href="https://www.alphaxiv.org/abs/2511.07934v1" target="_blank" rel="noopener noreferrer">
                Laytrol：在多模态扩散变换器中为布局控制保留预训练知识
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            Laytrol: Preserving Pretrained Knowledge in Layout Control for Multimodal Diffusion Transformers
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sida Huang, Siqi Huang, Ping Luo, Hongyuan Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态扩散模型中的布局控制技术，属于视觉生成领域。虽然涉及Transformer架构，但其核心应用是图像生成和布局控制，与推荐系统、搜索或广告中的排序和匹配任务没有直接关联。缺乏明确的机制表明这些技术可以应用于异构数据处理或推荐场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:31:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07934v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07934v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    With the development of diffusion models, enhancing spatial controllability in text-to-image generation has become a vital challenge. As a representative task for addressing this challenge, layout-to-image generation aims to generate images that are spatially consistent with the given layout condition. Existing layout-to-image methods typically introduce the layout condition by integrating adapter modules into the base generative model. However, the generated images often exhibit low visual quality and stylistic inconsistency with the base model, indicating a loss of pretrained knowledge. To alleviate this issue, we construct the Layout Synthesis (LaySyn) dataset, which leverages images synthesized by the base model itself to mitigate the distribution shift from the pretraining data. Moreover, we propose the Layout Control (Laytrol) Network, in which parameters are inherited from MM-DiT to preserve the pretrained knowledge of the base model. To effectively activate the copied parameters and avoid disturbance from unstable control conditions, we adopt a dedicated initialization scheme for Laytrol. In this scheme, the layout encoder is initialized as a pure text encoder to ensure that its output tokens remain within the data domain of MM-DiT. Meanwhile, the outputs of the layout control network are initialized to zero. In addition, we apply Object-level Rotary Position Embedding to the layout tokens to provide coarse positional information. Qualitative and quantitative experiments demonstrate the effectiveness of our method.
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            <a href="https://www.alphaxiv.org/abs/2511.07862v1" target="_blank" rel="noopener noreferrer">
                MonoCLUE：目标感知聚类增强单目3D目标检测
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            MonoCLUE : Object-Aware Clustering Enhances Monocular 3D Object Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sunghun Yang, Minhyeok Lee, Jungho Lee, Sangyoun Lee
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的单目3D目标检测技术，属于纯粹的视觉任务。虽然标题提到目标检测和聚类技术，但这些方法主要针对视觉场景理解，与推荐系统、搜索或广告中的排序、用户建模等核心问题没有直接关联。该技术缺乏明确的路径应用于异构数据处理或推荐系统架构中。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 05:59:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07862v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07862v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Monocular 3D object detection offers a cost-effective solution for autonomous driving but suffers from ill-posed depth and limited field of view. These constraints cause a lack of geometric cues and reduced accuracy in occluded or truncated scenes. While recent approaches incorporate additional depth information to address geometric ambiguity, they overlook the visual cues crucial for robust recognition. We propose MonoCLUE, which enhances monocular 3D detection by leveraging both local clustering and generalized scene memory of visual features. First, we perform K-means clustering on visual features to capture distinct object-level appearance parts (e.g., bonnet, car roof), improving detection of partially visible objects. The clustered features are propagated across regions to capture objects with similar appearances. Second, we construct a generalized scene memory by aggregating clustered features across images, providing consistent representations that generalize across scenes. This improves object-level feature consistency, enabling stable detection across varying environments. Lastly, we integrate both local cluster features and generalized scene memory into object queries, guiding attention toward informative regions. Exploiting a unified local clustering and generalized scene memory strategy, MonoCLUE enables robust monocular 3D detection under occlusion and limited visibility, achieving state-of-the-art performance on the KITTI benchmark.
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            <a href="https://www.alphaxiv.org/abs/2511.08461v1" target="_blank" rel="noopener noreferrer">
                生成式人工智能在定性研究方法中的应用：炒作与风险之间？
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        <div class="mb-2 text-base text-gray-700">
            Generative Artificial Intelligence in Qualitative Research Methods: Between Hype and Risks?
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Maria Couto Teixeira, Marisa Tschopp, Anna Jobin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于生成式AI在定性研究方法中的应用，这属于社会科学研究方法的范畴，而非推荐系统、搜索或广告领域的技术进展。论文讨论的是AI在学术研究中的潜在影响和风险，与我的关注领域（RecSys、搜索、广告的算法改进、Transformer架构、LLM应用等）没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:04:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08461v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08461v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CY</span><span class="category-tag">cs.CL</span></div>
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                    As Artificial Intelligence (AI) is increasingly promoted and used in qualitative research, it also raises profound methodological issues. This position paper critically interrogates the role of generative AI (genAI) in the context of qualitative coding methodologies. Despite widespread hype and claims of efficiency, we propose that genAI is not methodologically valid within qualitative inquiries, and its use risks undermining the robustness and trustworthiness of qualitative research. The lack of meaningful documentation, commercial opacity, and the inherent tendencies of genAI systems to produce incorrect outputs all contribute to weakening methodological rigor. Overall, the balance between risk and benefits does not support the use of genAI in qualitative research, and our position paper cautions researchers to put sound methodology before technological novelty.
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            <a href="https://www.alphaxiv.org/abs/2511.08092v1" target="_blank" rel="noopener noreferrer">
                剪枝作为正则化：ASR中的敏感度感知一次性剪枝
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        <div class="mb-2 text-base text-gray-700">
            Pruning as Regularization: Sensitivity-Aware One-Shot Pruning in ASR
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Julian Irigoyen, Arthur Söhler, Andreas Søeborg Kirkedal
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动语音识别(ASR)领域的模型剪枝技术，属于语音处理领域。虽然剪枝技术本身具有通用性，但论文明确限定在ASR应用场景，与搜索、推荐或广告系统的核心需求没有直接关联。语音识别属于被排除的无关主题范畴，无法建立与推荐系统、搜索或广告的技术联系。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:45:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08092v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08092v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We challenge the conventional view of neural network pruning as solely a compression technique, demonstrating that one-shot magnitude pruning serves as a powerful implicit regularizer for ASR. Using Whisper-small, we combine gradient- and Fisher-based sensitivity diagnostics with targeted, component-wise pruning. This reveals architectural asymmetries: decoder FFNs are pruning-fragile, whereas decoder self-attention and the last encoder layers contain redundancy that, when removed, improves generalization. Without fine-tuning, pruning 50% of decoder self-attention reduces WER by 2.38% absolute (20.44% relative) on LibriSpeech test-other; pruning the last four encoder layers at 50% instead yields a 1.72% absolute (14.8% relative) improvement. Gains persisted on Common Voice and TED-LIUM datasets. Beyond regularization benefits, our sensitivity-aware approach enables more aggressive one-shot compression. At 40% sparsity, where established global pruning approaches catastrophically fail, our method preserves near-baseline accuracy. This positions pruning as a first-class architectural design tool: knowing where to prune is as important as how much to prune.
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            <a href="https://www.alphaxiv.org/abs/2511.07876v1" target="_blank" rel="noopener noreferrer">
                LoopLLM：通过重复生成实现LLM中可迁移的能耗-延迟攻击
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        <div class="mb-2 text-base text-gray-700">
            LoopLLM: Transferable Energy-Latency Attacks in LLMs via Repetitive Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xingyu Li, Xiaolei Liu, Cheng Liu, Yixiao Xu, Kangyi Ding, Bangzhou Xin, Jia-Li ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM的安全攻击（能耗-延迟攻击），这属于安全/隐私领域，在无关主题中明确排除。虽然涉及LLM技术，但核心关注点是对抗性攻击而非LLM在推荐/搜索/广告中的应用或架构改进，与当前关注的核心领域进展、使能技术或直接应用无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:24:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07876v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07876v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As large language models (LLMs) scale, their inference incurs substantial computational resources, exposing them to energy-latency attacks, where crafted prompts induce high energy and latency cost. Existing attack methods aim to prolong output by delaying the generation of termination symbols. However, as the output grows longer, controlling the termination symbols through input becomes difficult, making these methods less effective. Therefore, we propose LoopLLM, an energy-latency attack framework based on the observation that repetitive generation can trigger low-entropy decoding loops, reliably compelling LLMs to generate until their output limits. LoopLLM introduces (1) a repetition-inducing prompt optimization that exploits autoregressive vulnerabilities to induce repetitive generation, and (2) a token-aligned ensemble optimization that aggregates gradients to improve cross-model transferability. Extensive experiments on 12 open-source and 2 commercial LLMs show that LoopLLM significantly outperforms existing methods, achieving over 90% of the maximum output length, compared to 20% for baselines, and improving transferability by around 40% to DeepSeek-V3 and Gemini 2.5 Flash.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07722v1" target="_blank" rel="noopener noreferrer">
                关键虚构：大型语言模型能否为公益产生幻觉？
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Critical Confabulation: Can LLMs Hallucinate for Social Good?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Peiqi Sui, Eamon Duede, Hoyt Long, Richard Jean So
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及幻觉（hallucination）主题，这属于明确排除的非相关主题范畴。虽然提到了LLMs，但核心关注点是幻觉的社会应用，与推荐系统、搜索或广告中的核心进展、使能技术或直接应用无关。该论文更偏向伦理和社会影响讨论，而非技术改进或应用创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 01:02:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07722v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07722v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    LLMs hallucinate, yet some confabulations can have social affordances if carefully bounded. We propose critical confabulation (inspired by critical fabulation from literary and social theory), the use of LLM hallucinations to "fill-in-the-gap" for omissions in archives due to social and political inequality, and reconstruct divergent yet evidence-bound narratives for history's "hidden figures". We simulate these gaps with an open-ended narrative cloze task: asking LLMs to generate a masked event in a character-centric timeline sourced from a novel corpus of unpublished texts. We evaluate audited (for data contamination), fully-open models (the OLMo-2 family) and unaudited open-weight and proprietary baselines under a range of prompts designed to elicit controlled and useful hallucinations. Our findings validate LLMs' foundational narrative understanding capabilities to perform critical confabulation, and show how controlled and well-specified hallucinations can support LLM applications for knowledge production without collapsing speculation into a lack of historical accuracy and fidelity.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08565v1" target="_blank" rel="noopener noreferrer">
                大型语言模型中人格角色扮演下的道德易感性与鲁棒性
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Davi Bastos Costa, Felippe Alves, Renato Vicente
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM在角色扮演场景中的道德敏感性和鲁棒性，这属于纯粹的LLM伦理和安全研究范畴。根据用户明确的排除标准，伦理、公平性等非技术性话题以及纯粹的LLM中心话题均被视为无关主题，因此该论文与用户关注的推荐系统、搜索、广告技术核心进展完全无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:47:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08565v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08565v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CY</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) increasingly operate in social contexts, motivating analysis of how they express and shift moral judgments. In this work, we investigate the moral response of LLMs to persona role-play, prompting a LLM to assume a specific character. Using the Moral Foundations Questionnaire (MFQ), we introduce a benchmark that quantifies two properties: moral susceptibility and moral robustness, defined from the variability of MFQ scores across and within personas, respectively. We find that, for moral robustness, model family accounts for most of the variance, while model size shows no systematic effect. The Claude family is, by a significant margin, the most robust, followed by Gemini and GPT-4 models, with other families exhibiting lower robustness. In contrast, moral susceptibility exhibits a mild family effect but a clear within-family size effect, with larger variants being more susceptible. Moreover, robustness and susceptibility are positively correlated, an association that is more pronounced at the family level. Additionally, we present moral foundation profiles for models without persona role-play and for personas averaged across models. Together, these analyses provide a systematic view of how persona conditioning shapes moral behavior in large language models.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08487v1" target="_blank" rel="noopener noreferrer">
                智能体安全性有多脆弱？在意图隐藏和任务复杂性下重新思考智能体风险
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        <div class="mb-2 text-base text-gray-700">
            How Brittle is Agent Safety? Rethinking Agent Risk under Intent Concealment and Task Complexity
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihan Ma, Dongsheng Zhu, Shudong Liu, Taolin Zhang, Junnan Liu, Qingqiu Li, Minn...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注智能体安全性和风险评估，属于AI安全领域，与推荐系统、搜索或广告的核心技术进展无关。论文讨论的意图隐藏和任务复杂性风险评估没有明确的推荐/搜索/广告应用场景，属于被排除的安全相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:27:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08487v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08487v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MA</span><span class="category-tag">cs.CL</span></div>
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                    Current safety evaluations for LLM-driven agents primarily focus on atomic harms, failing to address sophisticated threats where malicious intent is concealed or diluted within complex tasks. We address this gap with a two-dimensional analysis of agent safety brittleness under the orthogonal pressures of intent concealment and task complexity. To enable this, we introduce OASIS (Orthogonal Agent Safety Inquiry Suite), a hierarchical benchmark with fine-grained annotations and a high-fidelity simulation sandbox. Our findings reveal two critical phenomena: safety alignment degrades sharply and predictably as intent becomes obscured, and a "Complexity Paradox" emerges, where agents seem safer on harder tasks only due to capability limitations. By releasing OASIS and its simulation environment, we provide a principled foundation for probing and strengthening agent safety in these overlooked dimensions.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.08372v1" target="_blank" rel="noopener noreferrer">
                动态发音模型DYNARTmo：动态运动生成与语音姿态
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        <div class="mb-2 text-base text-gray-700">
            The Dynamic Articulatory Model DYNARTmo: Dynamic Movement Generation and Speech Gestures
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bernd J. Kröger
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音生成和发音建模，属于纯粹的语音处理领域。虽然提到了动态运动生成，但这特指发音器官的物理运动，与推荐系统、搜索或广告中的用户行为序列建模没有关联。该技术没有明显的潜在应用场景来增强RecSys/Search/Ads系统。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:50:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08372v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08372v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This paper describes the current implementation of the dynamic articulatory model DYNARTmo, which generates continuous articulator movements based on the concept of speech gestures and a corresponding gesture score. The model provides a neurobiologically inspired computational framework for simulating the hierarchical control of speech production from linguistic representation to articulatory-acoustic realization. We present the structure of the gesture inventory, the coordination of gestures in the gesture score, and their translation into continuous articulator trajectories controlling the DYNARTmo vocal tract model.
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            <a href="https://www.alphaxiv.org/abs/2511.08247v1" target="_blank" rel="noopener noreferrer">
                ParliaBench：面向大语言模型生成议会演讲的评估与基准框架
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            ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marios Koniaris, Argyro Tsipi, Panayiotis Tsanakas
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM生成内容的评估基准，属于纯粹的NLP评估范畴，与推荐系统、搜索或广告的核心技术进展无关。论文标题明确指向议会演讲生成和评估，没有任何技术组件或方法表明其在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:43:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08247v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08247v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Parliamentary speech generation presents specific challenges for large language models beyond standard text generation tasks. Unlike general text generation, parliamentary speeches require not only linguistic quality but also political authenticity and ideological consistency. Current language models lack specialized training for parliamentary contexts, and existing evaluation methods focus on standard NLP metrics rather than political authenticity. To address this, we present ParliaBench, a benchmark for parliamentary speech generation. We constructed a dataset of speeches from UK Parliament to enable systematic model training. We introduce an evaluation framework combining computational metrics with LLM-as-a-judge assessments for measuring generation quality across three dimensions: linguistic quality, semantic coherence, and political authenticity. We propose two novel embedding-based metrics, Political Spectrum Alignment and Party Alignment, to quantify ideological positioning. We fine-tuned five large language models (LLMs), generated 28k speeches, and evaluated them using our framework, comparing baseline and fine-tuned models. Results show that fine-tuning produces statistically significant improvements across the majority of metrics and our novel metrics demonstrate strong discriminative power for political dimensions.
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            <a href="https://www.alphaxiv.org/abs/2511.08242v1" target="_blank" rel="noopener noreferrer">
                面向结果导向、任务无关的AI智能体评估
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        <div class="mb-2 text-base text-gray-700">
            Towards Outcome-Oriented, Task-Agnostic Evaluation of AI Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Waseem AlShikh, Muayad Sayed Ali, Brian Kennedy, Dmytro Mozolevskyi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于AI智能体的通用评估方法，属于评估基准范畴，这在无关主题中被明确排除。虽然评估对任何AI系统都很重要，但该标题未提及推荐系统、搜索、广告或相关技术架构，也没有暗示在推荐/搜索/广告领域的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:40:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08242v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08242v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As AI agents proliferate across industries and applications, evaluating their performance based solely on infrastructural metrics such as latency, time-to-first-token, or token throughput is proving insufficient. These metrics fail to capture the quality of an agent's decisions, its operational autonomy, or its ultimate business value. This white paper proposes a novel, comprehensive framework of eleven outcome-based, task-agnostic performance metrics for AI agents that transcend domain boundaries. These metrics are designed to enable organizations to evaluate agents based on the quality of their decisions, their degree of autonomy, their adaptability to new challenges, and the tangible business value they deliver, regardless of the underlying model architecture or specific use case. We introduce metrics such as Goal Completion Rate (GCR), Autonomy Index (AIx), Multi-Step Task Resilience (MTR), and Business Impact Efficiency (BIE). Through a large-scale simulated experiment involving four distinct agent architectures (ReAct, Chain-of-Thought, Tool-Augmented, Hybrid) across five diverse domains (Healthcare, Finance, Marketing, Legal, and Customer Service), we demonstrate the framework's efficacy. Our results reveal significant performance trade-offs between different agent designs, highlighting the Hybrid Agent as the most consistently high-performing model across the majority of our proposed metrics, achieving an average Goal Completion Rate of 88.8\% and the highest Return on Investment (ROI). This work provides a robust, standardized methodology for the holistic evaluation of AI agents, paving the way for more effective development, deployment, and governance.
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            <a href="https://www.alphaxiv.org/abs/2511.08225v1" target="_blank" rel="noopener noreferrer">
                基于分析的教育大语言模型基准测试：关于反馈中性别偏见的案例研究
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            Benchmarking Educational LLMs with Analytics: A Case Study on Gender Bias in Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yishan Du, Conrad Borchers, Mutlu Cukurova
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注教育领域LLM的基准测试和性别偏见评估，属于纯粹的NLP评估基准主题。论文内容聚焦于特定领域（教育）的偏见分析，与搜索、推荐或广告系统的核心技术进步、LLM架构改进或直接应用完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:28:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08225v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08225v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.HC</span></div>
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                    As teachers increasingly turn to GenAI in their educational practice, we need robust methods to benchmark large language models (LLMs) for pedagogical purposes. This article presents an embedding-based benchmarking framework to detect bias in LLMs in the context of formative feedback. Using 600 authentic student essays from the AES 2.0 corpus, we constructed controlled counterfactuals along two dimensions: (i) implicit cues via lexicon-based swaps of gendered terms within essays, and (ii) explicit cues via gendered author background in the prompt. We investigated six representative LLMs (i.e. GPT-5 mini, GPT-4o mini, DeepSeek-R1, DeepSeek-R1-Qwen, Gemini 2.5 Pro, Llama-3-8B). We first quantified the response divergence with cosine and Euclidean distances over sentence embeddings, then assessed significance via permutation tests, and finally, visualised structure using dimensionality reduction. In all models, implicit manipulations reliably induced larger semantic shifts for male-female counterfactuals than for female-male. Only the GPT and Llama models showed sensitivity to explicit gender cues. These findings show that even state-of-the-art LLMs exhibit asymmetric semantic responses to gender substitutions, suggesting persistent gender biases in feedback they provide learners. Qualitative analyses further revealed consistent linguistic differences (e.g., more autonomy-supportive feedback under male cues vs. more controlling feedback under female cues). We discuss implications for fairness auditing of pedagogical GenAI, propose reporting standards for counterfactual evaluation in learning analytics, and outline practical guidance for prompt design and deployment to safeguard equitable feedback.
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            <a href="https://www.alphaxiv.org/abs/2511.07931v1" target="_blank" rel="noopener noreferrer">
                SpeechJudge：面向语音自然度的人类水平评判
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            SpeechJudge: Towards Human-Level Judgment for Speech Naturalness
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xueyao Zhang, Chaoren Wang, Huan Liao, Ziniu Li, Yuancheng Wang, Li Wang, Dongya...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音自然度的评估，属于纯语音处理领域，与推荐系统、搜索或广告没有明确关联。论文内容不涉及任何异构数据建模、Transformer架构改进或LLM技术在推荐/搜索/广告领域的潜在应用，完全落在无关主题范围内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:27:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07931v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07931v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Aligning large generative models with human feedback is a critical challenge. In speech synthesis, this is particularly pronounced due to the lack of a large-scale human preference dataset, which hinders the development of models that truly align with human perception. To address this, we introduce SpeechJudge, a comprehensive suite comprising a dataset, a benchmark, and a reward model centered on naturalness--one of the most fundamental subjective metrics for speech synthesis. First, we present SpeechJudge-Data, a large-scale human feedback corpus of 99K speech pairs. The dataset is constructed using a diverse set of advanced zero-shot text-to-speech (TTS) models across diverse speech styles and multiple languages, with human annotations for both intelligibility and naturalness preference. From this, we establish SpeechJudge-Eval, a challenging benchmark for speech naturalness judgment. Our evaluation reveals that existing metrics and AudioLLMs struggle with this task; the leading model, Gemini-2.5-Flash, achieves less than 70% agreement with human judgment, highlighting a significant gap for improvement. To bridge this gap, we develop SpeechJudge-GRM, a generative reward model (GRM) based on Qwen2.5-Omni-7B. It is trained on SpeechJudge-Data via a two-stage post-training process: Supervised Fine-Tuning (SFT) with Chain-of-Thought rationales followed by Reinforcement Learning (RL) with GRPO on challenging cases. On the SpeechJudge-Eval benchmark, the proposed SpeechJudge-GRM demonstrates superior performance, achieving 77.2% accuracy (and 79.4% after inference-time scaling @10) compared to a classic Bradley-Terry reward model (72.7%). Furthermore, SpeechJudge-GRM can be also employed as a reward function during the post-training of speech generation models to facilitate their alignment with human preferences.
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            <a href="https://www.alphaxiv.org/abs/2511.07914v1" target="_blank" rel="noopener noreferrer">
                社交媒体用于心理健康：数据、方法与发现
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            Social Media for Mental Health: Data, Methods, and Findings
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nur Shazwani Kamarudin, Ghazaleh Beigi, Lydia Manikonda, Huan Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于心理健康应用领域，这属于医疗/生物领域的特定应用，属于明确的无关主题。论文内容涉及社交媒体数据，但应用方向是心理健康而非推荐系统、搜索或广告的核心技术。没有迹象表明该研究涉及LLM技术、Transformer架构或推荐系统的核心进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:10:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07914v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07914v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like de- pression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.
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            <a href="https://www.alphaxiv.org/abs/2511.07888v1" target="_blank" rel="noopener noreferrer">
                通过流形净化打破文本分类中对抗鲁棒性与性能的权衡
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            Breaking the Adversarial Robustness-Performance Trade-off in Text Classification via Manifold Purification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenhao Dang, Jing Ma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本分类中的对抗鲁棒性，这是一个安全/防御性主题，明确属于被排除的无关主题范畴。虽然文本分类在搜索和推荐中有应用，但对抗鲁棒性本身是安全领域的技术，与当前关注的推荐系统、搜索、广告的核心算法进展、LLM技术或Transformer架构改进没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:39:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07888v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07888v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    A persistent challenge in text classification (TC) is that enhancing model robustness against adversarial attacks typically degrades performance on clean data. We argue that this challenge can be resolved by modeling the distribution of clean samples in the encoder embedding manifold. To this end, we propose the Manifold-Correcting Causal Flow (MC^2F), a two-module system that operates directly on sentence embeddings. A Stratified Riemannian Continuous Normalizing Flow (SR-CNF) learns the density of the clean data manifold. It identifies out-of-distribution embeddings, which are then corrected by a Geodesic Purification Solver. This solver projects adversarial points back onto the learned manifold via the shortest path, restoring a clean, semantically coherent representation. We conducted extensive evaluations on text classification (TC) across three datasets and multiple adversarial attacks. The results demonstrate that our method, MC^2F, not only establishes a new state-of-the-art in adversarial robustness but also fully preserves performance on clean data, even yielding modest gains in accuracy.
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                全球首个保险大语言模型评估基准的设计、结果与行业启示
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            Design, Results and Industry Implications of the World's First Insurance Large Language Model Evaluation Benchmark
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hua Zhou, Bing Ma, Yufei Zhang, Yi Zhao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于保险领域的LLM评估基准，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及LLM评估，但明确针对保险行业，与RecSys/Search/Ads的核心技术进展无关，且评估基准属于被排除的纯NLP中心主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:19:35
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                <a href="https://arxiv.org/abs/2511.07794v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07794v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    This paper comprehensively elaborates on the construction methodology, multi-dimensional evaluation system, and underlying design philosophy of CUFEInse v1.0. Adhering to the principles of "quantitative-oriented, expert-driven, and multi-validation," the benchmark establishes an evaluation framework covering 5 core dimensions, 54 sub-indicators, and 14,430 high-quality questions, encompassing insurance theoretical knowledge, industry understanding, safety and compliance, intelligent agent application, and logical rigor. Based on this benchmark, a comprehensive evaluation was conducted on 11 mainstream large language models. The evaluation results reveal that general-purpose models suffer from common bottlenecks such as weak actuarial capabilities and inadequate compliance adaptation. High-quality domain-specific training demonstrates significant advantages in insurance vertical scenarios but exhibits shortcomings in business adaptation and compliance. The evaluation also accurately identifies the common bottlenecks of current large models in professional scenarios such as insurance actuarial, underwriting and claim settlement reasoning, and compliant marketing copywriting. The establishment of CUFEInse not only fills the gap in professional evaluation benchmarks for the insurance field, providing academia and industry with a professional, systematic, and authoritative evaluation tool, but also its construction concept and methodology offer important references for the evaluation paradigm of large models in vertical fields, serving as an authoritative reference for academic model optimization and industrial model selection. Finally, the paper looks forward to the future iteration direction of the evaluation benchmark and the core development direction of "domain adaptation + reasoning enhancement" for insurance large models.
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                BARD10：一项新基准揭示孟加拉语停用词在作者归属中的重要性
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            BARD10: A New Benchmark Reveals Significance of Bangla Stop-Words in Authorship Attribution
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abdullah Muhammad Moosa, Nusrat Sultana, Mahdi Muhammad Moosa, Md. Miraiz Hossai...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于孟加拉语作者归属的基准测试和停用词分析，属于特定语言NLP任务。这与推荐系统、搜索或广告的核心技术焦点无关，也不涉及LLM、Transformer架构进展或异构数据统一建模。该研究纯粹是语言特征分析，没有展示在RecSys/Search/Ads领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:39:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08085v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08085v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This research presents a comprehensive investigation into Bangla authorship attribution, introducing a new balanced benchmark corpus BARD10 (Bangla Authorship Recognition Dataset of 10 authors) and systematically analyzing the impact of stop-word removal across classical and deep learning models to uncover the stylistic significance of Bangla stop-words. BARD10 is a curated corpus of Bangla blog and opinion prose from ten contemporary authors, alongside the methodical assessment of four representative classifiers: SVM (Support Vector Machine), Bangla BERT (Bidirectional Encoder Representations from Transformers), XGBoost, and a MLP (Multilayer Perception), utilizing uniform preprocessing on both BARD10 and the benchmark corpora BAAD16 (Bangla Authorship Attribution Dataset of 16 authors). In all datasets, the classical TF-IDF + SVM baseline outperformed, attaining a macro-F1 score of 0.997 on BAAD16 and 0.921 on BARD10, while Bangla BERT lagged by as much as five points. This study reveals that BARD10 authors are highly sensitive to stop-word pruning, while BAAD16 authors remain comparatively robust highlighting genre-dependent reliance on stop-word signatures. Error analysis revealed that high frequency components transmit authorial signatures that are diminished or reduced by transformer models. Three insights are identified: Bangla stop-words serve as essential stylistic indicators; finely calibrated ML models prove effective within short-text limitations; and BARD10 connects formal literature with contemporary web dialogue, offering a reproducible benchmark for future long-context or domain-adapted transformers.
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            <a href="https://www.alphaxiv.org/abs/2511.07918v1" target="_blank" rel="noopener noreferrer">
                不同语音模式下的独特Theta同步：感知、口语、耳语和想象
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        <div class="mb-2 text-base text-gray-700">
            Distinct Theta Synchrony across Speech Modes: Perceived, Spoken, Whispered, and Imagined
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jung-Sun Lee, Ha-Na Jo, Eunyeong Ko
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究语音处理中的神经同步机制，属于认知神经科学领域。内容涉及语音感知和生成的大脑活动模式，与推荐系统、搜索或广告的技术焦点完全无关。论文没有展示任何在推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:13:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07918v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07918v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Human speech production encompasses multiple modes such as perceived, overt, whispered, and imagined, each reflecting distinct neural mechanisms. Among these, theta-band synchrony has been closely associated with language processing, attentional control, and inner speech. However, previous studies have largely focused on a single mode, such as overt speech, and have rarely conducted an integrated comparison of theta synchrony across different speech modes. In this study, we analyzed differences in theta-band neural synchrony across speech modes based on connectivity metrics, focusing on region-wise variations. The results revealed that overt and whispered speech exhibited broader and stronger frontotemporal synchrony, reflecting active motor-phonological coupling during overt articulation, whereas perceived speech showed dominant posterior and temporal synchrony patterns, consistent with auditory perception and comprehension processes. In contrast, imagined speech demonstrated a more spatially confined but internally coherent synchronization pattern, primarily involving frontal and supplementary motor regions. These findings indicate that the extent and spatial distribution of theta synchrony differ substantially across modes, with overt articulation engaging widespread cortical interactions, whispered speech showing intermediate engagement, and perception relying predominantly on temporoparietal networks. Therefore, this study aims to elucidate the differences in theta-band neural synchrony across various speech modes, thereby uncovering both the shared and distinct neural dynamics underlying language perception and imagined speech.
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            <a href="https://www.alphaxiv.org/abs/2511.08052v1" target="_blank" rel="noopener noreferrer">
                用于增强大语言模型代码调试的双过程支架推理
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            Dual-Process Scaffold Reasoning for Enhancing LLM Code Debugging
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Po-Chung Hsieh, Chin-Po Chen, Jeng-Lin Li, Ming-Ching Chang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM在代码调试领域的应用，这属于纯粹的LLM应用场景，与推荐系统、搜索或广告的核心技术无关。论文内容涉及代码调试这一特定领域，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:55:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08052v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08052v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.SE</span></div>
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                    Recent LLMs have demonstrated sophisticated problem-solving capabilities on various benchmarks through advanced reasoning algorithms. However, the key research question of identifying reasoning steps that balance complexity and computational efficiency remains unsolved. Recent research has increasingly drawn upon psychological theories to explore strategies for optimizing cognitive pathways. The LLM's final outputs and intermediate steps are regarded as System 1 and System 2, respectively. However, an in-depth exploration of the System 2 reasoning is still lacking. Therefore, we propose a novel psychologically backed Scaffold Reasoning framework for code debugging, which encompasses the Scaffold Stream, Analytic Stream, and Integration Stream. The construction of reference code within the Scaffold Stream is integrated with the buggy code analysis results produced by the Analytic Stream through the Integration Stream. Our framework achieves an 88.91% pass rate and an average inference time of 5.36 seconds per-problem on DebugBench, outperforming other reasoning approaches across various LLMs in both reasoning accuracy and efficiency. Further analyses elucidate the advantages and limitations of various cognitive pathways across varying problem difficulties and bug types. Our findings also corroborate the alignment of the proposed Scaffold Reasoning framework with human cognitive processes.
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            <a href="https://www.alphaxiv.org/abs/2511.08389v1" target="_blank" rel="noopener noreferrer">
                统一模型与层融合用于语音基础模型
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        <div class="mb-2 text-base text-gray-700">
            Unifying Model and Layer Fusion for Speech Foundation Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yi-Jen Shih, David Harwath
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于语音基础模型的模型与层融合技术，属于纯粹的语音处理领域。虽然提到了模型融合技术，但语音模态与推荐系统、搜索或广告的核心技术栈关联性极弱，且论文标题未表明任何向多模态或文本处理方向的扩展潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:08:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08389v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08389v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Speech Foundation Models have gained significant attention recently. Prior works have shown that the fusion of representations from multiple layers of the same model or the fusion of multiple models can improve performance on downstream tasks. We unify these two fusion strategies by proposing an interface module that enables fusion across multiple upstream speech models while integrating information across their layers. We conduct extensive experiments on different self-supervised and supervised models across various speech tasks, including ASR and paralinguistic analysis, and demonstrate that our method outperforms prior fusion approaches. We further analyze its scalability concerning model size and count, highlighting the importance of selecting appropriate upstream models. Our results show that the proposed interface provides an additional performance boost when given a suitable upstream model selection, making it a promising approach for utilizing Speech Foundation Models.
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            <a href="https://www.alphaxiv.org/abs/2511.08230v1" target="_blank" rel="noopener noreferrer">
                VocalBench-zh：分解与基准测试普通话语境下的语音对话能力
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            VocalBench-zh: Decomposing and Benchmarking the Speech Conversational Abilities in Mandarin Context
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Heyang Liu, Ziyang Cheng, Yuhao Wang, Hongcheng Liu, Yiqi Li, Ronghua Wu, Qunsha...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音对话能力的基准测试，属于纯粹的语音处理领域，与推荐系统、搜索或广告的核心技术没有直接关联。论文内容涉及普通话语境下的语音能力评估，属于语音技术范畴，不符合任何当前关注的技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:30:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08230v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08230v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The development of multi-modal large language models (LLMs) leads to intelligent approaches capable of speech interactions. As one of the most widely spoken languages globally, Mandarin is supported by most models to enhance their applicability and reach. However, the scarcity of comprehensive speech-to-speech (S2S) benchmarks in Mandarin contexts impedes systematic evaluation for developers and hinders fair model comparison for users. In this work, we propose VocalBench-zh, an ability-level divided evaluation suite adapted to Mandarin context consisting of 10 well-crafted subsets and over 10K high-quality instances, covering 12 user-oriented characters. The evaluation experiment on 14 mainstream models reveals the common challenges for current routes, and highlights the need for new insights into next-generation speech interactive systems. The evaluation codes and datasets will be available at https://github.com/SJTU-OmniAgent/VocalBench-zh.
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            <a href="https://www.alphaxiv.org/abs/2511.07871v1" target="_blank" rel="noopener noreferrer">
                AlignSurvey：面向社会调查中人类偏好对齐的综合基准
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            AlignSurvey: A Comprehensive Benchmark for Human Preferences Alignment in Social Surveys
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenxi Lin, Weikang Yuan, Zhuoren Jiang, Biao Huang, Ruitao Zhang, Jianan Ge, Yu...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于社会调查领域的人类偏好对齐基准，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及偏好对齐概念，但缺乏与推荐系统、搜索或广告的直接技术关联，且社会调查应用超出了当前关注的技术范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 06:14:21
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                <a href="https://arxiv.org/abs/2511.07871v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07871v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Yet traditional surveys face persistent challenges, including fixed-question formats, high costs, limited adaptability, and difficulties ensuring cross-cultural equivalence. While recent studies explore large language models (LLMs) to simulate survey responses, most are limited to structured questions, overlook the entire survey process, and risks under-representing marginalized groups due to training data biases. We introduce AlignSurvey, the first benchmark that systematically replicates and evaluates the full social survey pipeline using LLMs. It defines four tasks aligned with key survey stages: social role modeling, semi-structured interview modeling, attitude stance modeling and survey response modeling. It also provides task-specific evaluation metrics to assess alignment fidelity, consistency, and fairness at both individual and group levels, with a focus on demographic diversity. To support AlignSurvey, we construct a multi-tiered dataset architecture: (i) the Social Foundation Corpus, a cross-national resource with 44K+ interview dialogues and 400K+ structured survey records; and (ii) a suite of Entire-Pipeline Survey Datasets, including the expert-annotated AlignSurvey-Expert (ASE) and two nationally representative surveys for cross-cultural evaluation. We release the SurveyLM family, obtained through two-stage fine-tuning of open-source LLMs, and offer reference models for evaluating domain-specific alignment. All datasets, models, and tools are available at github and huggingface to support transparent and socially responsible research.
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            <a href="https://www.alphaxiv.org/abs/2511.08507v1" target="_blank" rel="noopener noreferrer">
                用于孟加拉手语翻译与研究的孟加拉语句子-手语词汇配对数据集介绍
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        <div class="mb-2 text-base text-gray-700">
            Introducing A Bangla Sentence - Gloss Pair Dataset for Bangla Sign Language Translation and Research
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Neelavro Saha, Rafi Shahriyar, Nafis Ashraf Roudra, Saadman Sakib, Annajiat Alim...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定语言（孟加拉语）的手语翻译数据集创建，属于语言学和辅助技术领域。这与搜索、推荐或广告系统的核心进展、LLM技术或Transformer架构改进完全无关，也不涉及异构数据的统一建模方法。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:41:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08507v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08507v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Bangla Sign Language (BdSL) translation represents a low-resource NLP task due to the lack of large-scale datasets that address sentence-level translation. Correspondingly, existing research in this field has been limited to word and alphabet level detection. In this work, we introduce Bangla-SGP, a novel parallel dataset consisting of 1,000 human-annotated sentence-gloss pairs which was augmented with around 3,000 synthetically generated pairs using syntactic and morphological rules through a rule-based Retrieval-Augmented Generation (RAG) pipeline. The gloss sequences of the spoken Bangla sentences are made up of individual glosses which are Bangla sign supported words and serve as an intermediate representation for a continuous sign. Our dataset consists of 1000 high quality Bangla sentences that are manually annotated into a gloss sequence by a professional signer. The augmentation process incorporates rule-based linguistic strategies and prompt engineering techniques that we have adopted by critically analyzing our human annotated sentence-gloss pairs and by working closely with our professional signer. Furthermore, we fine-tune several transformer-based models such as mBart50, Google mT5, GPT4.1-nano and evaluate their sentence-to-gloss translation performance using BLEU scores, based on these evaluation metrics we compare the model's gloss-translation consistency across our dataset and the RWTH-PHOENIX-2014T benchmark.
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            <a href="https://www.alphaxiv.org/abs/2511.07790v1" target="_blank" rel="noopener noreferrer">
                CC30k：一个面向可复现性情感分析的引文上下文数据集
            </a>
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            CC30k: A Citation Contexts Dataset for Reproducibility-Oriented Sentiment Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于学术引文的情感分析数据集构建，属于纯粹的NLP评估基准研究。这与当前关注的推荐系统、搜索广告核心进展、LLM技术应用或Transformer架构改进等方向完全无关。该工作没有展示任何在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:13:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07790v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07790v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DL</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Sentiments about the reproducibility of cited papers in downstream literature offer community perspectives and have shown as a promising signal of the actual reproducibility of published findings. To train effective models to effectively predict reproducibility-oriented sentiments and further systematically study their correlation with reproducibility, we introduce the CC30k dataset, comprising a total of 30,734 citation contexts in machine learning papers. Each citation context is labeled with one of three reproducibility-oriented sentiment labels: Positive, Negative, or Neutral, reflecting the cited paper's perceived reproducibility or replicability. Of these, 25,829 are labeled through crowdsourcing, supplemented with negatives generated through a controlled pipeline to counter the scarcity of negative labels. Unlike traditional sentiment analysis datasets, CC30k focuses on reproducibility-oriented sentiments, addressing a research gap in resources for computational reproducibility studies. The dataset was created through a pipeline that includes robust data cleansing, careful crowd selection, and thorough validation. The resulting dataset achieves a labeling accuracy of 94%. We then demonstrated that the performance of three large language models significantly improves on the reproducibility-oriented sentiment classification after fine-tuning using our dataset. The dataset lays the foundation for large-scale assessments of the reproducibility of machine learning papers. The CC30k dataset and the Jupyter notebooks used to produce and analyze the dataset are publicly available at https://github.com/lamps-lab/CC30k .
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            <a href="https://www.alphaxiv.org/abs/2511.07732v1" target="_blank" rel="noopener noreferrer">
                ViPRA：用于机器人动作的视频预测
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            ViPRA: Video Prediction for Robot Actions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sandeep Routray, Hengkai Pan, Unnat Jain, Shikhar Bahl, Deepak Pathak
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器人领域的视频预测技术，与推荐系统、搜索或广告的核心领域进展、LLM技术或Transformer架构改进均无直接关联。视频预测在机器人动作规划中的应用场景过于特定，无法迁移到异构数据处理或多模态建模等RecSys相关技术中。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 01:33:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07732v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07732v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Can we turn a video prediction model into a robot policy? Videos, including those of humans or teleoperated robots, capture rich physical interactions. However, most of them lack labeled actions, which limits their use in robot learning. We present Video Prediction for Robot Actions (ViPRA), a simple pretraining-finetuning framework that learns continuous robot control from these actionless videos. Instead of directly predicting actions, we train a video-language model to predict both future visual observations and motion-centric latent actions, which serve as intermediate representations of scene dynamics. We train these latent actions using perceptual losses and optical flow consistency to ensure they reflect physically grounded behavior. For downstream control, we introduce a chunked flow matching decoder that maps latent actions to robot-specific continuous action sequences, using only 100 to 200 teleoperated demonstrations. This approach avoids expensive action annotation, supports generalization across embodiments, and enables smooth, high-frequency continuous control upto 22 Hz via chunked action decoding. Unlike prior latent action works that treat pretraining as autoregressive policy learning, explicitly models both what changes and how. Our method outperforms strong baselines, with a 16% gain on the SIMPLER benchmark and a 13% improvement across real world manipulation tasks. We will release models and code at https://vipra-project.github.io
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            <a href="https://www.alphaxiv.org/abs/2511.08093v1" target="_blank" rel="noopener noreferrer">
                量化Whisper-small：设计选择如何影响自动语音识别性能
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            Quantizing Whisper-small: How design choices affect ASR performance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arthur Söhler, Julian Irigoyen, Andreas Søeborg Kirkedal
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音识别模型的量化技术，属于纯粹的语音处理领域。虽然量化是模型效率的重要技术，但论文标题明确指向语音识别应用，没有显示出与推荐系统、搜索或广告领域的潜在关联。语音识别本身被列为不相关主题，除非有明确证据表明应用于相关领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:47:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08093v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08093v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.SD</span></div>
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                    Large speech recognition models like Whisper-small achieve high accuracy but are difficult to deploy on edge devices due to their high computational demand. To this end, we present a unified, cross-library evaluation of post-training quantization (PTQ) on Whisper-small that disentangles the impact of quantization scheme, method, granularity, and bit-width. Our study is based on four libraries: PyTorch, Optimum-Quanto, HQQ, and bitsandbytes. Experiments on LibriSpeech test-clean and test-other show that dynamic int8 quantization with Quanto offers the best trade-off, reducing model size by 57% while improving on the baseline's word error rate. Static quantization performed worse, likely due to Whisper's Transformer architecture, while more aggressive formats (e.g., nf4, int3) achieved up to 71% compression at the cost of accuracy in noisy conditions. Overall, our results demonstrate that carefully chosen PTQ methods can substantially reduce model size and inference cost without retraining, enabling efficient deployment of Whisper-small on constrained hardware.
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            <a href="https://www.alphaxiv.org/abs/2511.08365v1" target="_blank" rel="noopener noreferrer">
                基于解纠缠嵌入的磁共振成像回顾性运动校正
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            Retrospective motion correction in MRI using disentangled embeddings
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qi Wang, Veronika Ecker, Marcel Früh, Sergios Gatidis, Thomas Küstner
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（MRI）中的运动校正技术，这属于医学/生物医学领域的具体应用。论文内容涉及图像处理和计算机视觉技术，但没有显示出与推荐系统、搜索或广告领域的任何潜在关联。解纠缠嵌入技术虽然有趣，但在这个特定应用中缺乏向RecSys/Search/Ads领域转化的明显路径。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:44:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08365v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08365v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Physiological motion can affect the diagnostic quality of magnetic resonance imaging (MRI). While various retrospective motion correction methods exist, many struggle to generalize across different motion types and body regions. In particular, machine learning (ML)-based corrections are often tailored to specific applications and datasets. We hypothesize that motion artifacts, though diverse, share underlying patterns that can be disentangled and exploited. To address this, we propose a hierarchical vector-quantized (VQ) variational auto-encoder that learns a disentangled embedding of motion-to-clean image features. A codebook is deployed to capture finite collection of motion patterns at multiple resolutions, enabling coarse-to-fine correction. An auto-regressive model is trained to learn the prior distribution of motion-free images and is used at inference to guide the correction process. Unlike conventional approaches, our method does not require artifact-specific training and can generalize to unseen motion patterns. We demonstrate the approach on simulated whole-body motion artifacts and observe robust correction across varying motion severity. Our results suggest that the model effectively disentangled physical motion of the simulated motion-effective scans, therefore, improving the generalizability of the ML-based MRI motion correction. Our work of disentangling the motion features shed a light on its potential application across anatomical regions and motion types.
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            <a href="https://www.alphaxiv.org/abs/2511.08344v1" target="_blank" rel="noopener noreferrer">
                基于双视角不一致学习的开放集肌电手势识别研究
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            Towards Open-Set Myoelectric Gesture Recognition via Dual-Perspective Inconsistency Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chen Liu, Can Han, Weishi Xu, Yaqi Wang, Dahong Qian
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物医学工程领域的肌电手势识别，属于特定领域的信号处理应用。虽然涉及开放集识别技术，但其核心应用场景（医疗康复、人机交互）与搜索、推荐、广告系统没有直接关联。论文中提到的双视角不一致学习方法在推荐系统中没有明显的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:22:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08344v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08344v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span></div>
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                    Surface electromyography (sEMG)-based gesture recognition plays a critical role in human-machine interaction (HMI), particularly for rehabilitation and prosthetic control. However, sEMG-based systems often suffer from the scarcity of informative training data, leading to overfitting and poor generalization in deep learning models. Data augmentation offers a promising approach to increasing the size and diversity of training data, where faithfulness and diversity are two critical factors to effectiveness. However, promoting untargeted diversity can result in redundant samples with limited utility. To address these challenges, we propose a novel diffusion-based data augmentation approach, Sparse-Aware Semantic-Guided Diffusion Augmentation (SASG-DA). To enhance generation faithfulness, we introduce the Semantic Representation Guidance (SRG) mechanism by leveraging fine-grained, task-aware semantic representations as generation conditions. To enable flexible and diverse sample generation, we propose a Gaussian Modeling Semantic Modeling (GMSS) strategy, which models the semantic representation distribution and allows stochastic sampling to produce both faithful and diverse samples. To enhance targeted diversity, we further introduce a Sparse-Aware Semantic Sampling strategy to explicitly explore underrepresented regions, improving distribution coverage and sample utility. Extensive experiments on benchmark sEMG datasets, Ninapro DB2, DB4, and DB7, demonstrate that SASG-DA significantly outperforms existing augmentation methods. Overall, our proposed data augmentation approach effectively mitigates overfitting and improves recognition performance and generalization by offering both faithful and diverse samples.
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            <a href="https://www.alphaxiv.org/abs/2511.08269v1" target="_blank" rel="noopener noreferrer">
                针对不确定性的重新编码：面向弹性事件-RGB分割的边缘感知语义一致性
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        <div class="mb-2 text-base text-gray-700">
            Re-coding for Uncertainties: Edge-awareness Semantic Concordance for Resilient Event-RGB Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nan Bao, Yifan Zhao, Lin Zhu, Jia Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于事件相机与RGB视觉模态的语义分割，属于计算机视觉领域的特定应用。虽然提到了多模态处理，但这是纯粹的视觉模态组合（事件流+RGB图像），与推荐系统、搜索或广告中的异构数据建模没有直接关联。论文内容主要涉及视觉分割的鲁棒性技术，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:00:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08269v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08269v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Semantic segmentation has achieved great success in ideal conditions. However, when facing extreme conditions (e.g., insufficient light, fierce camera motion), most existing methods suffer from significant information loss of RGB, severely damaging segmentation results. Several researches exploit the high-speed and high-dynamic event modality as a complement, but event and RGB are naturally heterogeneous, which leads to feature-level mismatch and inferior optimization of existing multi-modality methods. Different from these researches, we delve into the edge secret of both modalities for resilient fusion and propose a novel Edge-awareness Semantic Concordance framework to unify the multi-modality heterogeneous features with latent edge cues. In this framework, we first propose Edge-awareness Latent Re-coding, which obtains uncertainty indicators while realigning event-RGB features into unified semantic space guided by re-coded distribution, and transfers event-RGB distributions into re-coded features by utilizing a pre-established edge dictionary as clues. We then propose Re-coded Consolidation and Uncertainty Optimization, which utilize re-coded edge features and uncertainty indicators to solve the heterogeneous event-RGB fusion issues under extreme conditions. We establish two synthetic and one real-world event-RGB semantic segmentation datasets for extreme scenario comparisons. Experimental results show that our method outperforms the state-of-the-art by a 2.55% mIoU on our proposed DERS-XS, and possesses superior resilience under spatial occlusion. Our code and datasets are publicly available at https://github.com/iCVTEAM/ESC.
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            <a href="https://www.alphaxiv.org/abs/2511.08240v1" target="_blank" rel="noopener noreferrer">
                基于原子点积算子的分层方向感知用于旋转不变点云学习
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Hierarchical Direction Perception via Atomic Dot-Product Operators for Rotation-Invariant Point Clouds Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenyu Hu, Xiaotong Li, Hao Zhu, Biao Hou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的点云处理，属于纯粹的3D视觉领域，与推荐系统、搜索或广告没有直接关联。论文提出的旋转不变点云学习技术主要面向3D物体识别和场景理解等视觉任务，无法看出在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:38:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08240v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08240v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Point cloud processing has become a cornerstone technology in many 3D vision tasks. However, arbitrary rotations introduce variations in point cloud orientations, posing a long-standing challenge for effective representation learning. The core of this issue is the disruption of the point cloud's intrinsic directional characteristics caused by rotational perturbations. Recent methods attempt to implicitly model rotational equivariance and invariance, preserving directional information and propagating it into deep semantic spaces. Yet, they often fall short of fully exploiting the multiscale directional nature of point clouds to enhance feature representations. To address this, we propose the Direction-Perceptive Vector Network (DiPVNet). At its core is an atomic dot-product operator that simultaneously encodes directional selectivity and rotation invariance--endowing the network with both rotational symmetry modeling and adaptive directional perception. At the local level, we introduce a Learnable Local Dot-Product (L2DP) Operator, which enables interactions between a center point and its neighbors to adaptively capture the non-uniform local structures of point clouds. At the global level, we leverage generalized harmonic analysis to prove that the dot-product between point clouds and spherical sampling vectors is equivalent to a direction-aware spherical Fourier transform (DASFT). This leads to the construction of a global directional response spectrum for modeling holistic directional structures. We rigorously prove the rotation invariance of both operators. Extensive experiments on challenging scenarios involving noise and large-angle rotations demonstrate that DiPVNet achieves state-of-the-art performance on point cloud classification and segmentation tasks. Our code is available at https://github.com/wxszreal0/DiPVNet.
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            <a href="https://www.alphaxiv.org/abs/2511.08224v1" target="_blank" rel="noopener noreferrer">
                用于无引导单视图3D超分辨率的实时2D表示
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ignasi Mas, Ivan Huerta, Ramon Morros, Javier Ruiz-Hidalgo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于3D视觉和超分辨率技术，属于纯粹的计算机视觉领域。虽然提到了2D表示，但其核心应用场景是3D重建和图像处理，与推荐系统、搜索或广告没有明确的关联。该技术没有展示出在异构数据处理或推荐系统应用方面的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:27:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08224v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08224v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    We introduce 2Dto3D-SR, a versatile framework for real-time single-view 3D super-resolution that eliminates the need for high-resolution RGB guidance. Our framework encodes 3D data from a single viewpoint into a structured 2D representation, enabling the direct application of existing 2D image super-resolution architectures. We utilize the Projected Normalized Coordinate Code (PNCC) to represent 3D geometry from a visible surface as a regular image, thereby circumventing the complexities of 3D point-based or RGB-guided methods. This design supports lightweight and fast models adaptable to various deployment environments. We evaluate 2Dto3D-SR with two implementations: one using Swin Transformers for high accuracy, and another using Vision Mamba for high efficiency. Experiments show the Swin Transformer model achieves state-of-the-art accuracy on standard benchmarks, while the Vision Mamba model delivers competitive results at real-time speeds. This establishes our geometry-guided pipeline as a surprisingly simple yet viable and practical solution for real-world scenarios, especially where high-resolution RGB data is inaccessible.
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            <a href="https://www.alphaxiv.org/abs/2511.08203v1" target="_blank" rel="noopener noreferrer">
                扭曲与计算：3D生成扩散中姿态的成本
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Twist and Compute: The Cost of Pose in 3D Generative Diffusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kyle Fogarty, Jack Foster, Boqiao Zhang, Jing Yang, Cengiz Öztireli
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于3D生成和扩散模型中的姿态处理，属于纯粹的计算机视觉和3D生成领域。虽然提到了扩散模型技术，但内容主要围绕3D姿态和生成，与推荐系统、搜索或广告的核心技术栈没有明显关联，也没有展示在异构数据建模或Transformer架构方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:08:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08203v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08203v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Despite their impressive results, large-scale image-to-3D generative models remain opaque in their inductive biases. We identify a significant limitation in image-conditioned 3D generative models: a strong canonical view bias. Through controlled experiments using simple 2D rotations, we show that the state-of-the-art Hunyuan3D 2.0 model can struggle to generalize across viewpoints, with performance degrading under rotated inputs. We show that this failure can be mitigated by a lightweight CNN that detects and corrects input orientation, restoring model performance without modifying the generative backbone. Our findings raise an important open question: Is scale enough, or should we pursue modular, symmetry-aware designs?
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            <a href="https://www.alphaxiv.org/abs/2511.08140v1" target="_blank" rel="noopener noreferrer">
                PEOD：面向挑战性条件下目标检测的像素对齐事件-RGB基准
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        <div class="mb-2 text-base text-gray-700">
            PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luoping Cui, Hanqing Liu, Mingjie Liu, Endian Lin, Donghong Jiang, Yuhao Wang, C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的目标检测，特别是事件相机与RGB数据的融合，属于纯粹的视觉研究领域。论文内容涉及基准数据集创建和视觉感知技术，与推荐系统、搜索或广告的核心技术栈没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等当前关注的技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:50:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08140v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08140v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (<= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the first large-scale, pixel-aligned and high-resolution (1280 x 720) Event-RGB dataset for object detection under challenge conditions. PEOD contains 130+ spatiotemporal-aligned sequences and 340k manual bounding boxes, with 57% of data captured under low-light, overexposure, and high-speed motion. Furthermore, we benchmark 14 methods across three input configurations (Event-based, RGB-based, and Event-RGB fusion) on PEOD. On the full test set and normal subset, fusion-based models achieve the excellent performance. However, in illumination challenge subset, the top event-based model outperforms all fusion models, while fusion models still outperform their RGB-based counterparts, indicating limits of existing fusion methods when the frame modality is severely degraded. PEOD establishes a realistic, high-quality benchmark for multimodal perception and facilitates future research.
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            <a href="https://www.alphaxiv.org/abs/2511.08114v1" target="_blank" rel="noopener noreferrer">
                引入尼龙面罩攻击：用于评估广义人脸呈现攻击检测的数据集
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            Introducing Nylon Face Mask Attacks: A Dataset for Evaluating Generalised Face Presentation Attack Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manasa, Sushrut Patwardhan, Narayan Vetrekar, Pavan Kumar, R. S. Gad, Raghavendr...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于人脸呈现攻击检测和安全验证领域，属于计算机视觉中的生物识别安全方向。这与推荐系统、搜索或广告的核心技术领域完全无关，也不涉及LLM、Transformer架构或异构数据建模等当前关注的技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:13:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08114v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08114v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.ET</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Face recognition systems are increasingly deployed across a wide range of applications, including smartphone authentication, access control, and border security. However, these systems remain vulnerable to presentation attacks (PAs), which can significantly compromise their reliability. In this work, we introduce a new dataset focused on a novel and realistic presentation attack instrument called Nylon Face Masks (NFMs), designed to simulate advanced 3D spoofing scenarios. NFMs are particularly concerning due to their elastic structure and photorealistic appearance, which enable them to closely mimic the victim's facial geometry when worn by an attacker. To reflect real-world smartphone-based usage conditions, we collected the dataset using an iPhone 11 Pro, capturing 3,760 bona fide samples from 100 subjects and 51,281 NFM attack samples across four distinct presentation scenarios involving both humans and mannequins. We benchmark the dataset using five state-of-the-art PAD methods to evaluate their robustness under unseen attack conditions. The results demonstrate significant performance variability across methods, highlighting the challenges posed by NFMs and underscoring the importance of developing PAD techniques that generalise effectively to emerging spoofing threats.
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            <a href="https://www.alphaxiv.org/abs/2511.08090v1" target="_blank" rel="noopener noreferrer">
                StableMorph：基于稳定扩散的高质量人脸融合生成
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            StableMorph: High-Quality Face Morph Generation with Stable Diffusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wassim Kabbani, Kiran Raja, Raghavendra Ramachandra, Christoph Busch
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人脸图像生成和编辑技术，属于纯粹的视觉内容生成范畴。虽然使用了扩散模型技术，但论文的应用场景（人脸融合生成）与推荐系统、搜索或广告的排名和匹配任务没有直接关联，也不涉及处理异构数据或多模态建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:44:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08090v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08090v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Face morphing attacks threaten the integrity of biometric identity systems by enabling multiple individuals to share a single identity. To develop and evaluate effective morphing attack detection (MAD) systems, we need access to high-quality, realistic morphed images that reflect the challenges posed in real-world scenarios. However, existing morph generation methods often produce images that are blurry, riddled with artifacts, or poorly constructed making them easy to detect and not representative of the most dangerous attacks. In this work, we introduce StableMorph, a novel approach that generates highly realistic, artifact-free morphed face images using modern diffusion-based image synthesis. Unlike prior methods, StableMorph produces full-head images with sharp details, avoids common visual flaws, and offers unmatched control over visual attributes. Through extensive evaluation, we show that StableMorph images not only rival or exceed the quality of genuine face images but also maintain a strong ability to fool face recognition systems posing a greater challenge to existing MAD solutions and setting a new standard for morph quality in research and operational testing. StableMorph improves the evaluation of biometric security by creating more realistic and effective attacks and supports the development of more robust detection systems.
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            <a href="https://www.alphaxiv.org/abs/2511.08061v1" target="_blank" rel="noopener noreferrer">
                通过潜在连接和掩码条件流匹配在扩散模型中驯服身份一致性和提示多样性
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        <div class="mb-2 text-base text-gray-700">
            Taming Identity Consistency and Prompt Diversity in Diffusion Models via Latent Concatenation and Masked Conditional Flow Matching
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aditi Singhania, Arushi Jain, Krutik Malani, Riddhi Dhawan, Souymodip Chakrabort...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于扩散模型的改进，属于纯粹的生成式AI领域，与推荐系统、搜索或广告的核心技术无关。虽然扩散模型可以用于广告创意生成，但这属于明确排除的非排名广告主题，没有直接应用于排名、检索或推荐系统的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:00:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08061v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08061v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Subject-driven image generation aims to synthesize novel depictions of a specific subject across diverse contexts while preserving its core identity features. Achieving both strong identity consistency and high prompt diversity presents a fundamental trade-off. We propose a LoRA fine-tuned diffusion model employing a latent concatenation strategy, which jointly processes reference and target images, combined with a masked Conditional Flow Matching (CFM) objective. This approach enables robust identity preservation without architectural modifications. To facilitate large-scale training, we introduce a two-stage Distilled Data Curation Framework: the first stage leverages data restoration and VLM-based filtering to create a compact, high-quality seed dataset from diverse sources; the second stage utilizes these curated examples for parameter-efficient fine-tuning, thus scaling the generation capability across various subjects and contexts. Finally, for filtering and quality assessment, we present CHARIS, a fine-grained evaluation framework that performs attribute-level comparisons along five key axes: identity consistency, prompt adherence, region-wise color fidelity, visual quality, and transformation diversity.
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            <a href="https://www.alphaxiv.org/abs/2511.08018v1" target="_blank" rel="noopener noreferrer">
                高质量候选框编码与级联去噪的想象监督目标检测
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            High-Quality Proposal Encoding and Cascade Denoising for Imaginary Supervised Object Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiyuan Chen, Yuelin Guo, Zitong Huang, Haoyu He, Renhao Lu, Weizhe Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的目标检测任务，特别是涉及图像监督和去噪技术。虽然标题提到了'编码'和'级联'等概念，但核心应用领域是纯粹的计算机视觉，与推荐系统、搜索或广告没有明确的关联。该技术没有显示出在异构数据处理或推荐系统应用方面的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:19:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08018v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08018v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Object detection models demand large-scale annotated datasets, which are costly and labor-intensive to create. This motivated Imaginary Supervised Object Detection (ISOD), where models train on synthetic images and test on real images. However, existing methods face three limitations: (1) synthetic datasets suffer from simplistic prompts, poor image quality, and weak supervision; (2) DETR-based detectors, due to their random query initialization, struggle with slow convergence and overfitting to synthetic patterns, hindering real-world generalization; (3) uniform denoising pressure promotes model overfitting to pseudo-label noise. We propose Cascade HQP-DETR to address these limitations. First, we introduce a high-quality data pipeline using LLaMA-3, Flux, and Grounding DINO to generate the FluxVOC and FluxCOCO datasets, advancing ISOD from weak to full supervision. Second, our High-Quality Proposal guided query encoding initializes object queries with image-specific priors from SAM-generated proposals and RoI-pooled features, accelerating convergence while steering the model to learn transferable features instead of overfitting to synthetic patterns. Third, our cascade denoising algorithm dynamically adjusts training weights through progressively increasing IoU thresholds across decoder layers, guiding the model to learn robust boundaries from reliable visual cues rather than overfitting to noisy labels. Trained for just 12 epochs solely on FluxVOC, Cascade HQP-DETR achieves a SOTA 61.04\% mAP@0.5 on PASCAL VOC 2007, outperforming strong baselines, with its competitive real-data performance confirming the architecture's universal applicability.
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            <a href="https://www.alphaxiv.org/abs/2511.08015v1" target="_blank" rel="noopener noreferrer">
                隐形触发器，可见威胁！面向自动驾驶视觉3D检测的道路风格对抗创建攻击
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            Invisible Triggers, Visible Threats! Road-Style Adversarial Creation Attack for Visual 3D Detection in Autonomous Driving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jian Wang, Lijun He, Yixing Yong, Haixia Bi, Fan Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的视觉3D检测和对抗攻击，属于纯粹的计算机视觉应用。虽然涉及3D视觉技术，但缺乏与推荐系统、搜索或广告领域的明确关联。论文内容主要针对自动驾驶安全，不在当前关注的任何技术范畴内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:17:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08015v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08015v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Modern autonomous driving (AD) systems leverage 3D object detection to perceive foreground objects in 3D environments for subsequent prediction and planning. Visual 3D detection based on RGB cameras provides a cost-effective solution compared to the LiDAR paradigm. While achieving promising detection accuracy, current deep neural network-based models remain highly susceptible to adversarial examples. The underlying safety concerns motivate us to investigate realistic adversarial attacks in AD scenarios. Previous work has demonstrated the feasibility of placing adversarial posters on the road surface to induce hallucinations in the detector. However, the unnatural appearance of the posters makes them easily noticeable by humans, and their fixed content can be readily targeted and defended. To address these limitations, we propose the AdvRoad to generate diverse road-style adversarial posters. The adversaries have naturalistic appearances resembling the road surface while compromising the detector to perceive non-existent objects at the attack locations. We employ a two-stage approach, termed Road-Style Adversary Generation and Scenario-Associated Adaptation, to maximize the attack effectiveness on the input scene while ensuring the natural appearance of the poster, allowing the attack to be carried out stealthily without drawing human attention. Extensive experiments show that AdvRoad generalizes well to different detectors, scenes, and spoofing locations. Moreover, physical attacks further demonstrate the practical threats in real-world environments.
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            <a href="https://www.alphaxiv.org/abs/2511.07990v1" target="_blank" rel="noopener noreferrer">
                面向低功耗边缘AI的硬件感知YOLO压缩：用于STM32U5上数字农业杂草检测
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            Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Charalampos S. Kouzinopoulos, Yuri Manna
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的模型压缩和边缘AI部署，应用于农业杂草检测。这与我的关注点（推荐系统、搜索、广告以及相关的LLM/Transformer技术）完全无关，属于纯粹的视觉应用领域，且没有展示任何在RecSys/Search/Ads中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:55:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07990v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07990v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Weeds significantly reduce crop yields worldwide and pose major challenges to sustainable agriculture. Traditional weed management methods, primarily relying on chemical herbicides, risk environmental contamination and lead to the emergence of herbicide-resistant species. Precision weeding, leveraging computer vision and machine learning methods, offers a promising eco-friendly alternative but is often limited by reliance on high-power computational platforms. This work presents an optimized, low-power edge AI system for weeds detection based on the YOLOv8n object detector deployed on the STM32U575ZI microcontroller. Several compression techniques are applied to the detection model, including structured pruning, integer quantization and input image resolution scaling in order to meet strict hardware constraints. The model is trained and evaluated on the CropAndWeed dataset with 74 plant species, achieving a balanced trade-off between detection accuracy and efficiency. Our system supports real-time, in-situ weeds detection with a minimal energy consumption of 51.8mJ per inference, enabling scalable deployment in power-constrained agricultural environments.
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                对于个性化说话人脸生成，是否真正需要处理和拟合数分钟长的参考视频？
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            Is It Truly Necessary to Process and Fit Minutes-Long Reference Videos for Personalized Talking Face Generation?
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rui-Qing Sun, Ang Li, Zhijing Wu, Tian Lan, Qianyu Lu, Xingshan Yao, Chen Xu, Xi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于说话人脸生成的计算机视觉任务，属于纯粹的视觉内容生成领域。虽然涉及个性化概念，但这与推荐系统、搜索或广告中的用户个性化建模有本质区别，且没有展示出在推荐/搜索/广告领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:43:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07940v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07940v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Talking Face Generation (TFG) aims to produce realistic and dynamic talking portraits, with broad applications in fields such as digital education, film and television production, e-commerce live streaming, and other related areas. Currently, TFG methods based on Neural Radiated Field (NeRF) or 3D Gaussian sputtering (3DGS) are received widespread attention. They learn and store personalized features from reference videos of each target individual to generate realistic speaking videos. To ensure models can capture sufficient 3D information and successfully learns the lip-audio mapping, previous studies usually require meticulous processing and fitting several minutes of reference video, which always takes hours. The computational burden of processing and fitting long reference videos severely limits the practical application value of these methods.However, is it really necessary to fit such minutes of reference video? Our exploratory case studies show that using some informative reference video segments of just a few seconds can achieve performance comparable to or even better than the full reference video. This indicates that video informative quality is much more important than its length. Inspired by this observation, we propose the ISExplore (short for Informative Segment Explore), a simple-yet-effective segment selection strategy that automatically identifies the informative 5-second reference video segment based on three key data quality dimensions: audio feature diversity, lip movement amplitude, and number of camera views. Extensive experiments demonstrate that our approach increases data processing and training speed by more than 5x for NeRF and 3DGS methods, while maintaining high-fidelity output. Project resources are available at xx.
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            <a href="https://www.alphaxiv.org/abs/2511.07925v1" target="_blank" rel="noopener noreferrer">
                HD²-SSC：面向自动驾驶的高维高密度语义场景补全
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            HD$^2$-SSC: High-Dimension High-Density Semantic Scene Completion for Autonomous Driving
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiwen Yang, Yuxin Peng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的语义场景补全，属于纯粹的计算机视觉应用。虽然标题提到'高维高密度'数据处理，但这是针对3D点云和视觉场景的特定处理，与推荐系统、搜索或广告中的异构数据处理没有直接关联。该技术缺乏在RecSys/Search/Ads领域的潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:24:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07925v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07925v1
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                    Camera-based 3D semantic scene completion (SSC) plays a crucial role in autonomous driving, enabling voxelized 3D scene understanding for effective scene perception and decision-making. Existing SSC methods have shown efficacy in improving 3D scene representations, but suffer from the inherent input-output dimension gap and annotation-reality density gap, where the 2D planner view from input images with sparse annotated labels leads to inferior prediction of real-world dense occupancy with a 3D stereoscopic view. In light of this, we propose the corresponding High-Dimension High-Density Semantic Scene Completion (HD$^2$-SSC) framework with expanded pixel semantics and refined voxel occupancies. To bridge the dimension gap, a High-dimension Semantic Decoupling module is designed to expand 2D image features along a pseudo third dimension, decoupling coarse pixel semantics from occlusions, and then identify focal regions with fine semantics to enrich image features. To mitigate the density gap, a High-density Occupancy Refinement module is devised with a "detect-and-refine" architecture to leverage contextual geometric and semantic structures for enhanced semantic density with the completion of missing voxels and correction of erroneous ones. Extensive experiments and analyses on the SemanticKITTI and SSCBench-KITTI-360 datasets validate the effectiveness of our HD$^2$-SSC framework.
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            <a href="https://www.alphaxiv.org/abs/2511.07923v1" target="_blank" rel="noopener noreferrer">
                探索无需额外训练的水下世界分割
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            Exploring the Underwater World Segmentation without Extra Training
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bingyu Li, Tao Huo, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明研究焦点是计算机视觉中的水下图像分割，这属于纯粹的视觉任务，与推荐系统、搜索或广告没有明确的关联。水下分割技术缺乏在推荐、搜索或广告领域的潜在应用场景，因此被判定为不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:22:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07923v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07923v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain largely limited to terrestrial scenes. To bridge this gap, we introduce \textbf{AquaOV255}, the first large-scale and fine-grained underwater segmentation dataset containing 255 categories and over 20K images, covering diverse categories for open-vocabulary (OV) evaluation. Furthermore, we establish the first underwater OV segmentation benchmark, \textbf{UOVSBench}, by integrating AquaOV255 with five additional underwater datasets to enable comprehensive evaluation. Alongside, we present \textbf{Earth2Ocean}, a training-free OV segmentation framework that transfers terrestrial vision--language models (VLMs) to underwater domains without any additional underwater training. Earth2Ocean consists of two core components: a Geometric-guided Visual Mask Generator (\textbf{GMG}) that refines visual features via self-similarity geometric priors for local structure perception, and a Category-visual Semantic Alignment (\textbf{CSA}) module that enhances text embeddings through multimodal large language model reasoning and scene-aware template construction. Extensive experiments on the UOVSBench benchmark demonstrate that Earth2Ocean achieves significant performance improvement on average while maintaining efficient inference.
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            <a href="https://www.alphaxiv.org/abs/2511.07916v1" target="_blank" rel="noopener noreferrer">
                图像幂律变换在文本极性检测中的理论分析
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            Theoretical Analysis of Power-law Transformation on Images for Text Polarity Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Narendra Singh Yadav, Pavan Kumar Perepu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于图像处理和文本极性检测的理论分析，与推荐系统、搜索或广告的核心领域无关。虽然标题提及文本极性检测，但这属于纯粹的NLP任务，没有展示在推荐/搜索/广告领域的潜在应用价值。论文内容主要涉及图像变换和文本分析的理论研究，完全偏离了指定的关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:10:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07916v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07916v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Several computer vision applications like vehicle license plate recognition, captcha recognition, printed or handwriting character recognition from images etc., text polarity detection and binarization are the important preprocessing tasks. To analyze any image, it has to be converted to a simple binary image. This binarization process requires the knowledge of polarity of text in the images. Text polarity is defined as the contrast of text with respect to background. That means, text is darker than the background (dark text on bright background) or vice-versa. The binarization process uses this polarity information to convert the original colour or gray scale image into a binary image. In the literature, there is an intuitive approach based on power-law transformation on the original images. In this approach, the authors have illustrated an interesting phenomenon from the histogram statistics of the transformed images. Considering text and background as two classes, they have observed that maximum between-class variance between two classes is increasing (decreasing) for dark (bright) text on bright (dark) background. The corresponding empirical results have been presented. In this paper, we present a theoretical analysis of the above phenomenon.
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            <a href="https://www.alphaxiv.org/abs/2511.07820v1" target="_blank" rel="noopener noreferrer">
                SONIC：用于自然人形机器人全身控制的超大规模运动追踪
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            SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengyi Luo, Ye Yuan, Tingwu Wang, Chenran Li, Sirui Chen, Fernando Castañeda, Z...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器人运动控制和人体动作追踪，属于纯粹的机器人学和计算机视觉领域。虽然涉及动作追踪技术，但没有任何明确的连接或潜在应用指向推荐系统、搜索或广告领域。该研究的技术方向与用户行为建模、内容理解或个性化服务等RecSys/Search/Ads核心问题完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:37:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07820v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07820v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span><span class="category-tag">eess.SY</span></div>
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                    Despite the rise of billion-parameter foundation models trained across thousands of GPUs, similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, target a limited behavior set, and are trained on a handful of GPUs over several days. We show that scaling up model capacity, data, and compute yields a generalist humanoid controller capable of creating natural and robust whole-body movements. Specifically, we posit motion tracking as a natural and scalable task for humanoid control, leverageing dense supervision from diverse motion-capture data to acquire human motion priors without manual reward engineering. We build a foundation model for motion tracking by scaling along three axes: network size (from 1.2M to 42M parameters), dataset volume (over 100M frames, 700 hours of high-quality motion data), and compute (9k GPU hours). Beyond demonstrating the benefits of scale, we show the practical utility of our model through two mechanisms: (1) a real-time universal kinematic planner that bridges motion tracking to downstream task execution, enabling natural and interactive control, and (2) a unified token space that supports various motion input interfaces, such as VR teleoperation devices, human videos, and vision-language-action (VLA) models, all using the same policy. Scaling motion tracking exhibits favorable properties: performance improves steadily with increased compute and data diversity, and learned representations generalize to unseen motions, establishing motion tracking at scale as a practical foundation for humanoid control.
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            <a href="https://www.alphaxiv.org/abs/2511.07748v1" target="_blank" rel="noopener noreferrer">
                Auto-US：一种使用视频分类框架和大语言模型的超声视频诊断智能体
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            Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuezhe Yang, Yiyue Guo, Wenjie Cai, Qingqing Ruan, Siying Wang, Xingbo Dong, Zhe...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学超声视频诊断应用，属于明确的医疗领域特定应用，完全在无关主题范围内。论文虽然提及LLMs，但其应用场景是医疗诊断而非推荐系统、搜索或广告领域，没有任何与RecSys/Search/Ads相关的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 02:00:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07748v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07748v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    AI-assisted ultrasound video diagnosis presents new opportunities to enhance the efficiency and accuracy of medical imaging analysis. However, existing research remains limited in terms of dataset diversity, diagnostic performance, and clinical applicability. In this study, we propose \textbf{Auto-US}, an intelligent diagnosis agent that integrates ultrasound video data with clinical diagnostic text. To support this, we constructed \textbf{CUV Dataset} of 495 ultrasound videos spanning five categories and three organs, aggregated from multiple open-access sources. We developed \textbf{CTU-Net}, which achieves state-of-the-art performance in ultrasound video classification, reaching an accuracy of 86.73\% Furthermore, by incorporating large language models, Auto-US is capable of generating clinically meaningful diagnostic suggestions. The final diagnostic scores for each case exceeded 3 out of 5 and were validated by professional clinicians. These results demonstrate the effectiveness and clinical potential of Auto-US in real-world ultrasound applications. Code and data are available at: https://github.com/Bean-Young/Auto-US.
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                UltraGS：用于超声新视角合成的高斯泼溅技术
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            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            UltraGS: Gaussian Splatting for Ultrasound Novel View Synthesis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuezhe Yang, Wenjie Cai, Dexin Yang, Yufang Dong, Xingbo Dong, Zhe Jin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学超声成像领域的新视角合成技术，属于医学影像处理的特定应用。高斯泼溅技术虽然是一种3D重建方法，但论文明确聚焦于超声成像这一医疗领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。该技术缺乏在RecSys/Search/Ads领域的潜在应用场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 01:54:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07743v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07743v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Ultrasound imaging is a cornerstone of non-invasive clinical diagnostics, yet its limited field of view complicates novel view synthesis. We propose \textbf{UltraGS}, a Gaussian Splatting framework optimized for ultrasound imaging. First, we introduce a depth-aware Gaussian splatting strategy, where each Gaussian is assigned a learnable field of view, enabling accurate depth prediction and precise structural representation. Second, we design SH-DARS, a lightweight rendering function combining low-order spherical harmonics with ultrasound-specific wave physics, including depth attenuation, reflection, and scattering, to model tissue intensity accurately. Third, we contribute the Clinical Ultrasound Examination Dataset, a benchmark capturing diverse anatomical scans under real-world clinical protocols. Extensive experiments on three datasets demonstrate UltraGS's superiority, achieving state-of-the-art results in PSNR (up to 29.55), SSIM (up to 0.89), and MSE (as low as 0.002) while enabling real-time synthesis at 64.69 fps. The code and dataset are open-sourced at: https://github.com/Bean-Young/UltraGS.
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            <a href="https://www.alphaxiv.org/abs/2511.07941v1" target="_blank" rel="noopener noreferrer">
                Libra-MIL：融合任务特定语言先验的多模态原型立体注入用于少样本全切片图像分类
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Libra-MIL: Multimodal Prototypes Stereoscopic Infused with Task-specific Language Priors for Few-shot Whole Slide Image Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenfeng Zhuang, Fangyu Zhou, Liansheng Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分类（全切片图像），属于医疗领域特定应用，与推荐系统、搜索或广告无关。虽然提到了多模态和语言先验，但应用场景严格限定在医学诊断领域，没有展示任何在推荐、搜索或广告系统中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:46:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07941v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07941v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While Large Language Models (LLMs) are emerging as a promising direction in computational pathology, the substantial computational cost of giga-pixel Whole Slide Images (WSIs) necessitates the use of Multi-Instance Learning (MIL) to enable effective modeling. A key challenge is that pathological tasks typically provide only bag-level labels, while instance-level descriptions generated by LLMs often suffer from bias due to a lack of fine-grained medical knowledge. To address this, we propose that constructing task-specific pathological entity prototypes is crucial for learning generalizable features and enhancing model interpretability. Furthermore, existing vision-language MIL methods often employ unidirectional guidance, limiting cross-modal synergy. In this paper, we introduce a novel approach, Multimodal Prototype-based Multi-Instance Learning, that promotes bidirectional interaction through a balanced information compression scheme. Specifically, we leverage a frozen LLM to generate task-specific pathological entity descriptions, which are learned as text prototypes. Concurrently, the vision branch learns instance-level prototypes to mitigate the model's reliance on redundant data. For the fusion stage, we employ the Stereoscopic Optimal Transport (SOT) algorithm, which is based on a similarity metric, thereby facilitating broader semantic alignment in a higher-dimensional space. We conduct few-shot classification and explainability experiments on three distinct cancer datasets, and the results demonstrate the superior generalization capabilities of our proposed method.
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            <a href="https://www.alphaxiv.org/abs/2511.07823v1" target="_blank" rel="noopener noreferrer">
                CloudMamba：用于点云分析的分组选择性状态空间
            </a>
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            CloudMamba: Grouped Selective State Spaces for Point Cloud Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kanglin Qu, Pan Gao, Qun Dai, Zhanzhi Ye, Rui Ye, Yuanhao Sun
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于点云分析的特定计算机视觉任务，属于纯粹的3D视觉领域，与推荐系统、搜索或广告的核心技术焦点完全无关。论文提出的分组选择性状态空间机制是针对点云数据的专用架构，没有明确的路径可以应用于文本序列处理或用户行为建模等推荐/搜索相关场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:42:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07823v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07823v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Due to the long-range modeling ability and linear complexity property, Mamba has attracted considerable attention in point cloud analysis. Despite some interesting progress, related work still suffers from imperfect point cloud serialization, insufficient high-level geometric perception, and overfitting of the selective state space model (S6) at the core of Mamba. To this end, we resort to an SSM-based point cloud network termed CloudMamba to address the above challenges. Specifically, we propose sequence expanding and sequence merging, where the former serializes points along each axis separately and the latter serves to fuse the corresponding higher-order features causally inferred from different sequences, enabling unordered point sets to adapt more stably to the causal nature of Mamba without parameters. Meanwhile, we design chainedMamba that chains the forward and backward processes in the parallel bidirectional Mamba, capturing high-level geometric information during scanning. In addition, we propose a grouped selective state space model (GS6) via parameter sharing on S6, alleviating the overfitting problem caused by the computational mode in S6. Experiments on various point cloud tasks validate CloudMamba's ability to achieve state-of-the-art results with significantly less complexity.
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            <a href="https://www.alphaxiv.org/abs/2511.07816v1" target="_blank" rel="noopener noreferrer">
                Cancer-Net PCa-MultiSeg：使用合成相关扩散成像增强前列腺癌病灶分割的多模态方法
            </a>
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            Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jarett Dewbury, Chi-en Amy Tai, Alexander Wong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像中的前列腺癌病灶分割，属于明确的医学领域应用。虽然提到了多模态增强，但这与推荐系统、搜索或广告领域完全无关，也不涉及LLM或Transformer架构的任何相关技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:16:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07816v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07816v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance, with Dice scores of 0.32 or lower in large patient cohorts. To address this limitation, we investigate synthetic correlated diffusion imaging (CDI$^s$) as an enhancement to standard diffusion-based protocols. We conduct a comprehensive evaluation across six state-of-the-art segmentation architectures using 200 patients with co-registered CDI$^s$, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences. We demonstrate that CDI$^s$ integration reliably enhances or preserves segmentation performance in 94% of evaluated configurations, with individual architectures achieving up to 72.5% statistically significant relative improvement over baseline modalities. CDI$^s$ + DWI emerges as the safest enhancement pathway, achieving significant improvements in half of evaluated architectures with zero instances of degradation. Since CDI$^s$ derives from existing DWI acquisitions without requiring additional scan time or architectural modifications, it enables immediate deployment in clinical workflows. Our results establish validated integration pathways for CDI$^s$ as a practical drop-in enhancement for PCa lesion segmentation tasks across diverse deep learning architectures.
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            <a href="https://www.alphaxiv.org/abs/2511.07808v1" target="_blank" rel="noopener noreferrer">
                DI3CL：用于SAR土地覆盖分类基础模型的动态实例与轮廓一致性对比学习
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            DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhongle Ren, Hui Ding, Kai Wang, Biao Hou, Xingyu Luo, Weibin Li, Licheng Jiao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于SAR（合成孔径雷达）土地覆盖分类这一遥感应用领域，属于纯粹的地理信息/遥感技术范畴。虽然提到了基础模型和对比学习技术，但其应用场景（土地覆盖分类）与推荐系统、搜索或广告领域没有任何关联，完全超出了当前关注的技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:58:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07808v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07808v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Although significant advances have been achieved in SAR land-cover classification, recent methods remain predominantly focused on supervised learning, which relies heavily on extensive labeled datasets. This dependency not only limits scalability and generalization but also restricts adaptability to diverse application scenarios. In this paper, a general-purpose foundation model for SAR land-cover classification is developed, serving as a robust cornerstone to accelerate the development and deployment of various downstream models. Specifically, a Dynamic Instance and Contour Consistency Contrastive Learning (DI3CL) pre-training framework is presented, which incorporates a Dynamic Instance (DI) module and a Contour Consistency (CC) module. DI module enhances global contextual awareness by enforcing local consistency across different views of the same region. CC module leverages shallow feature maps to guide the model to focus on the geometric contours of SAR land-cover objects, thereby improving structural discrimination. Additionally, to enhance robustness and generalization during pre-training, a large-scale and diverse dataset named SARSense, comprising 460,532 SAR images, is constructed to enable the model to capture comprehensive and representative features. To evaluate the generalization capability of our foundation model, we conducted extensive experiments across a variety of SAR land-cover classification tasks, including SAR land-cover mapping, water body detection, and road extraction. The results consistently demonstrate that the proposed DI3CL outperforms existing methods. Our code and pre-trained weights are publicly available at: https://github.com/SARpre-train/DI3CL.
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            <a href="https://www.alphaxiv.org/abs/2511.08435v1" target="_blank" rel="noopener noreferrer">
                用于半监督医学图像分割的跨金字塔一致性正则化
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            Cross-pyramid consistency regularization for semi-supervised medical image segmentation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Matus Bojko, Maros Kollar, Marek Jakab, Wanda Benesova
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学图像分割，这是一个与生物医学领域相关的特定应用，完全超出了我的关注范围。论文标题中提到的半监督学习和一致性正则化技术虽然具有技术价值，但在标题层面没有显示出与推荐系统、搜索或广告的潜在应用联系。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:38:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08435v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08435v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Semi-supervised learning (SSL) enables training of powerful models with the assumption of limited, carefully labelled data and a large amount of unlabeled data to support the learning. In this paper, we propose a hybrid consistency learning approach to effectively exploit unlabeled data for semi-supervised medical image segmentation by leveraging Cross-Pyramid Consistency Regularization (CPCR) between two decoders. First, we design a hybrid Dual Branch Pyramid Network (DBPNet), consisting of an encoder and two decoders that differ slightly, each producing a pyramid of perturbed auxiliary predictions across multiple resolution scales. Second, we present a learning strategy for this network named CPCR that combines existing consistency learning and uncertainty minimization approaches on the main output predictions of decoders with our novel regularization term. More specifically, in this term, we extend the soft-labeling setting to pyramid predictions across decoders to support knowledge distillation in deep hierarchical features. Experimental results show that DBPNet with CPCR outperforms five state-of-the-art self-supervised learning methods and has comparable performance with recent ones on a public benchmark dataset.
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            <a href="https://www.alphaxiv.org/abs/2511.08258v1" target="_blank" rel="noopener noreferrer">
                Top2Ground：一种高度感知的双重条件扩散模型，用于鲁棒的空中到地面视图生成
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            Top2Ground: A Height-Aware Dual Conditioning Diffusion Model for Robust Aerial-to-Ground View Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jae Joong Lee, Bedrich Benes
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的特定视图生成任务（空中到地面视图转换），属于纯粹的视觉生成应用。虽然使用了扩散模型技术，但其应用场景与推荐系统、搜索或广告的核心技术需求没有明显关联。该技术缺乏在RecSys/Search/Ads领域的潜在应用前景，属于纯粹的视觉生成研究方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:53:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08258v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08258v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Generating ground-level images from aerial views is a challenging task due to extreme viewpoint disparity, occlusions, and a limited field of view. We introduce Top2Ground, a novel diffusion-based method that directly generates photorealistic ground-view images from aerial input images without relying on intermediate representations such as depth maps or 3D voxels. Specifically, we condition the denoising process on a joint representation of VAE-encoded spatial features (derived from aerial RGB images and an estimated height map) and CLIP-based semantic embeddings. This design ensures the generation is both geometrically constrained by the scene's 3D structure and semantically consistent with its content. We evaluate Top2Ground on three diverse datasets: CVUSA, CVACT, and the Auto Arborist. Our approach shows 7.3% average improvement in SSIM across three benchmark datasets, showing Top2Ground can robustly handle both wide and narrow fields of view, highlighting its strong generalization capabilities.
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            <a href="https://www.alphaxiv.org/abs/2511.08155v1" target="_blank" rel="noopener noreferrer">
                面向新视角合成的非对齐参考图像质量评估
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        <div class="mb-2 text-base text-gray-700">
            Non-Aligned Reference Image Quality Assessment for Novel View Synthesis
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Abhijay Ghildyal, Rajesh Sureddi, Nabajeet Barman, Saman Zadtootaghaj, Alan Bovi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的新视角合成和图像质量评估，与推荐系统、搜索或广告的核心技术没有直接关联。虽然图像质量评估在某些边缘场景可能有间接应用，但论文标题明确指向视觉生成任务，缺乏在推荐、搜索或广告领域的明确应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:08:12
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                <a href="https://arxiv.org/abs/2511.08155v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08155v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Evaluating the perceptual quality of Novel View Synthesis (NVS) images remains a key challenge, particularly in the absence of pixel-aligned ground truth references. Full-Reference Image Quality Assessment (FR-IQA) methods fail under misalignment, while No-Reference (NR-IQA) methods struggle with generalization. In this work, we introduce a Non-Aligned Reference (NAR-IQA) framework tailored for NVS, where it is assumed that the reference view shares partial scene content but lacks pixel-level alignment. We constructed a large-scale image dataset containing synthetic distortions targeting Temporal Regions of Interest (TROI) to train our NAR-IQA model. Our model is built on a contrastive learning framework that incorporates LoRA-enhanced DINOv2 embeddings and is guided by supervision from existing IQA methods. We train exclusively on synthetically generated distortions, deliberately avoiding overfitting to specific real NVS samples and thereby enhancing the model's generalization capability. Our model outperforms state-of-the-art FR-IQA, NR-IQA, and NAR-IQA methods, achieving robust performance on both aligned and non-aligned references. We also conducted a novel user study to gather data on human preferences when viewing non-aligned references in NVS. We find strong correlation between our proposed quality prediction model and the collected subjective ratings. For dataset and code, please visit our project page: https://stootaghaj.github.io/nova-project/
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            <a href="https://www.alphaxiv.org/abs/2511.08119v1" target="_blank" rel="noopener noreferrer">
                LatentPrintFormer：一种用于潜指纹识别的具有空间注意力的混合CNN-Transformer架构
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            LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Arnab Maity, Manasa, Pavan Kumar C, Raghavendra Ramachandra
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及指纹识别技术，这属于生物识别安全领域，与我的关注点（推荐系统、搜索、广告）完全无关。指纹识别属于安全/隐私范畴，已被明确列为不相关主题，没有任何潜在应用可以关联到推荐、搜索或广告领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 11:20:49
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                <a href="https://arxiv.org/abs/2511.08119v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08119v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints. A spatial attention module is employed to emphasize high-quality ridge regions while suppressing background noise. The extracted features are fused and projected into a unified 512-dimensional embedding, and matching is performed using cosine similarity in a closed-set identification setting. Extensive experiments on two publicly available datasets demonstrate that LatentPrintFormer consistently outperforms three state-of-the-art latent fingerprint recognition techniques, achieving higher identification rates across Rank-10.
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            <a href="https://www.alphaxiv.org/abs/2511.08054v1" target="_blank" rel="noopener noreferrer">
                Re²MaP：基于树结构重定位的递归原型生成与打包的宏布局方法
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            Re$^{\text{2}}$MaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunqi Shi, Xi Lin, Zhiang Wang, Siyuan Xu, Shixiong Kai, Yao Lai, Chengrui Gao, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文标题明显涉及芯片设计中的宏布局问题，属于电子设计自动化(EDA)领域。该主题与推荐系统、搜索、广告或LLM技术没有任何关联，完全超出了您关注的核心技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:56:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08054v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08054v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AR</span><span class="category-tag">cs.CV</span><span class="category-tag">eess.SY</span></div>
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                    This work introduces the Re$^{\text{2}}$MaP method, which generates expert-quality macro placements through recursively prototyping and packing tree-based relocating. We first perform multi-level macro grouping and PPA-aware cell clustering to produce a unified connection matrix that captures both wirelength and dataflow among macros and clusters. Next, we use DREAMPlace to build a mixed-size placement prototype and obtain reference positions for each macro and cluster. Based on this prototype, we introduce ABPlace, an angle-based analytical method that optimizes macro positions on an ellipse to distribute macros uniformly near chip periphery, while optimizing wirelength and dataflow. A packing tree-based relocating procedure is then designed to jointly adjust the locations of macro groups and the macros within each group, by optimizing an expertise-inspired cost function that captures various design constraints through evolutionary search. Re$^{\text{2}}$MaP repeats the above process: Only a subset of macro groups are positioned in each iteration, and the remaining macros are deferred to the next iteration to improve the prototype's accuracy. Using a well-established backend flow with sufficient timing optimizations, Re$^{\text{2}}$MaP achieves up to 22.22% (average 10.26%) improvement in worst negative slack (WNS) and up to 97.91% (average 33.97%) improvement in total negative slack (TNS) compared to the state-of-the-art academic placer Hier-RTLMP. It also ranks higher on WNS, TNS, power, design rule check (DRC) violations, and runtime than the conference version ReMaP, across seven tested cases. Our code is available at https://github.com/lamda-bbo/Re2MaP.
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            <a href="https://www.alphaxiv.org/abs/2511.08031v1" target="_blank" rel="noopener noreferrer">
                基于FPN-Transformer的多模态深度伪造检测与定位
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            Multi-modal Deepfake Detection and Localization with FPN-Transformer
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chende Zheng, Ruiqi Suo, Zhoulin Ji, Jingyi Deng, Fangbin Yi, Chenhao Lin, Chao ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多模态深度伪造检测和定位，属于计算机视觉安全领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了Transformer架构，但其应用场景（深度伪造检测）与我的关注领域（RecSys/Search/Ads）没有直接关联，且属于安全相关主题，属于明确排除的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:33:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08031v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08031v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    The rapid advancement of generative adversarial networks (GANs) and diffusion models has enabled the creation of highly realistic deepfake content, posing significant threats to digital trust across audio-visual domains. While unimodal detection methods have shown progress in identifying synthetic media, their inability to leverage cross-modal correlations and precisely localize forged segments limits their practicality against sophisticated, fine-grained manipulations. To address this, we introduce a multi-modal deepfake detection and localization framework based on a Feature Pyramid-Transformer (FPN-Transformer), addressing critical gaps in cross-modal generalization and temporal boundary regression. The proposed approach utilizes pre-trained self-supervised models (WavLM for audio, CLIP for video) to extract hierarchical temporal features. A multi-scale feature pyramid is constructed through R-TLM blocks with localized attention mechanisms, enabling joint analysis of cross-context temporal dependencies. The dual-branch prediction head simultaneously predicts forgery probabilities and refines temporal offsets of manipulated segments, achieving frame-level localization precision. We evaluate our approach on the test set of the IJCAI'25 DDL-AV benchmark, showing a good performance with a final score of 0.7535 for cross-modal deepfake detection and localization in challenging environments. Experimental results confirm the effectiveness of our approach and provide a novel way for generalized deepfake detection. Our code is available at https://github.com/Zig-HS/MM-DDL
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            <a href="https://www.alphaxiv.org/abs/2511.08007v1" target="_blank" rel="noopener noreferrer">
                EAGLE：用于以自我为中心视觉中统一2D-3D视觉查询定位的基于情景的外观和几何感知记忆
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            EAGLE: Episodic Appearance- and Geometry-aware Memory for Unified 2D-3D Visual Query Localization in Egocentric Vision
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yifei Cao, Yu Liu, Guolong Wang, Zhu Liu, Kai Wang, Xianjie Zhang, Jizhe Yu, Xun...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于以自我为中心的视觉和2D-3D定位任务，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告没有直接关联。标题中提到的外观和几何感知记忆技术主要针对视觉场景理解，在RecSys/Search/Ads领域没有明显的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:11:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08007v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08007v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Egocentric visual query localization is vital for embodied AI and VR/AR, yet remains challenging due to camera motion, viewpoint changes, and appearance variations. We present EAGLE, a novel framework that leverages episodic appearance- and geometry-aware memory to achieve unified 2D-3D visual query localization in egocentric vision. Inspired by avian memory consolidation, EAGLE synergistically integrates segmentation guided by an appearance-aware meta-learning memory (AMM), with tracking driven by a geometry-aware localization memory (GLM). This memory consolidation mechanism, through structured appearance and geometry memory banks, stores high-confidence retrieval samples, effectively supporting both long- and short-term modeling of target appearance variations. This enables precise contour delineation with robust spatial discrimination, leading to significantly improved retrieval accuracy. Furthermore, by integrating the VQL-2D output with a visual geometry grounded Transformer (VGGT), we achieve a efficient unification of 2D and 3D tasks, enabling rapid and accurate back-projection into 3D space. Our method achieves state-ofthe-art performance on the Ego4D-VQ benchmark.
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            <a href="https://www.alphaxiv.org/abs/2511.07987v1" target="_blank" rel="noopener noreferrer">
                CSF-Net：面向大规模掩码修复的上下文-语义融合网络
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            CSF-Net: Context-Semantic Fusion Network for Large Mask Inpainting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chae-Yeon Heo, Yeong-Jun Cho
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像修复任务，涉及大规模掩码填充和上下文语义融合技术。虽然标题提到'上下文'和'语义'概念，但这是纯粹的视觉修复问题，没有明确的技术路径或潜在应用与推荐系统、搜索或广告相关。该工作属于纯粹的视觉处理领域，不符合任何当前关注的技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:52:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07987v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07987v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In this paper, we propose a semantic-guided framework to address the challenging problem of large-mask image inpainting, where essential visual content is missing and contextual cues are limited. To compensate for the limited context, we leverage a pretrained Amodal Completion (AC) model to generate structure-aware candidates that serve as semantic priors for the missing regions. We introduce Context-Semantic Fusion Network (CSF-Net), a transformer-based fusion framework that fuses these candidates with contextual features to produce a semantic guidance image for image inpainting. This guidance improves inpainting quality by promoting structural accuracy and semantic consistency. CSF-Net can be seamlessly integrated into existing inpainting models without architectural changes and consistently enhances performance across diverse masking conditions. Extensive experiments on the Places365 and COCOA datasets demonstrate that CSF-Net effectively reduces object hallucination while enhancing visual realism and semantic alignment. The code for CSF-Net is available at https://github.com/chaeyeonheo/CSF-Net.
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            <a href="https://www.alphaxiv.org/abs/2511.07966v1" target="_blank" rel="noopener noreferrer">
                点云3D目标检测中无监督领域自适应的多模态辅助方法
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            Multi-Modal Assistance for Unsupervised Domain Adaptation on Point Cloud 3D Object Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shenao Zhao, Pengpeng Liang, Zhoufan Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于点云3D目标检测和领域自适应，属于纯粹的3D视觉领域，与推荐系统、搜索或广告没有直接关联。虽然提到了多模态，但上下文是3D点云处理，无法看出在异构数据处理或推荐系统应用方面的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:27:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07966v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07966v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unsupervised domain adaptation for LiDAR-based 3D object detection (3D UDA) based on the teacher-student architecture with pseudo labels has achieved notable improvements in recent years. Although it is quite popular to collect point clouds and images simultaneously, little attention has been paid to the usefulness of image data in 3D UDA when training the models. In this paper, we propose an approach named MMAssist that improves the performance of 3D UDA with multi-modal assistance. A method is designed to align 3D features between the source domain and the target domain by using image and text features as bridges. More specifically, we project the ground truth labels or pseudo labels to the images to get a set of 2D bounding boxes. For each 2D box, we extract its image feature from a pre-trained vision backbone. A large vision-language model (LVLM) is adopted to extract the box's text description, and a pre-trained text encoder is used to obtain its text feature. During the training of the model in the source domain and the student model in the target domain, we align the 3D features of the predicted boxes with their corresponding image and text features, and the 3D features and the aligned features are fused with learned weights for the final prediction. The features between the student branch and the teacher branch in the target domain are aligned as well. To enhance the pseudo labels, we use an off-the-shelf 2D object detector to generate 2D bounding boxes from images and estimate their corresponding 3D boxes with the aid of point cloud, and these 3D boxes are combined with the pseudo labels generated by the teacher model. Experimental results show that our approach achieves promising performance compared with state-of-the-art methods in three domain adaptation tasks on three popular 3D object detection datasets. The code is available at https://github.com/liangp/MMAssist.
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            <a href="https://www.alphaxiv.org/abs/2511.08585v1" target="_blank" rel="noopener noreferrer">
                用人工智能模拟视觉世界：路线图
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            Simulating the Visual World with Artificial Intelligence: A Roadmap
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingtong Yue, Ziqi Huang, Zhaoxi Chen, Xintao Wang, Pengfei Wan, Ziwei Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文标题聚焦于视觉世界的模拟和人工智能，属于纯粹的视觉领域研究。根据用户明确的排除标准，纯粹的视觉论文如果没有明确的推荐系统/搜索/广告相关性，应被视为不相关。该标题没有显示出与推荐系统、搜索或广告排名的直接联系。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:59:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08585v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08585v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence of video foundation models that function not only as visual generators but also as implicit world models, models that simulate the physical dynamics, agent-environment interactions, and task planning that govern real or imagined worlds. This survey provides a systematic overview of this evolution, conceptualizing modern video foundation models as the combination of two core components: an implicit world model and a video renderer. The world model encodes structured knowledge about the world, including physical laws, interaction dynamics, and agent behavior. It serves as a latent simulation engine that enables coherent visual reasoning, long-term temporal consistency, and goal-driven planning. The video renderer transforms this latent simulation into realistic visual observations, effectively producing videos as a "window" into the simulated world. We trace the progression of video generation through four generations, in which the core capabilities advance step by step, ultimately culminating in a world model, built upon a video generation model, that embodies intrinsic physical plausibility, real-time multimodal interaction, and planning capabilities spanning multiple spatiotemporal scales. For each generation, we define its core characteristics, highlight representative works, and examine their application domains such as robotics, autonomous driving, and interactive gaming. Finally, we discuss open challenges and design principles for next-generation world models, including the role of agent intelligence in shaping and evaluating these systems. An up-to-date list of related works is maintained at this link.
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            <a href="https://www.alphaxiv.org/abs/2511.08573v1" target="_blank" rel="noopener noreferrer">
                SENCA-st：通过交叉注意力共享编码器整合空间转录组学与组织病理学用于癌症病理学区域识别
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            SENCA-st: Integrating Spatial Transcriptomics and Histopathology with Cross Attention Shared Encoder for Region Identification in Cancer Pathology
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shanaka Liyanaarachchi, Chathurya Wijethunga, Shihab Aaquil Ahamed, Akthas Absar...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的癌症病理学分析，涉及空间转录组学和组织病理学等生物医学技术。这与我的关注领域（推荐系统、搜索、广告及相关LLM技术）完全无关，属于明确的医学/生物学应用范畴，属于应排除的无关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:54:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08573v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08573v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Spatial transcriptomics is an emerging field that enables the identification of functional regions based on the spatial distribution of gene expression. Integrating this functional information present in transcriptomic data with structural data from histopathology images is an active research area with applications in identifying tumor substructures associated with cancer drug resistance. Current histopathology-spatial-transcriptomic region segmentation methods suffer due to either making spatial transcriptomics prominent by using histopathology features just to assist processing spatial transcriptomics data or using vanilla contrastive learning that make histopathology images prominent due to only promoting common features losing functional information. In both extremes, the model gets either lost in the noise of spatial transcriptomics or overly smoothed, losing essential information. Thus, we propose our novel architecture SENCA-st (Shared Encoder with Neighborhood Cross Attention) that preserves the features of both modalities. More importantly, it emphasizes regions that are structurally similar in histopathology but functionally different on spatial transcriptomics using cross-attention. We demonstrate the superior performance of our model that surpasses state-of-the-art methods in detecting tumor heterogeneity and tumor micro-environment regions, a clinically crucial aspect.
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            <a href="https://www.alphaxiv.org/abs/2511.08545v1" target="_blank" rel="noopener noreferrer">
                RePose-NeRF：在噪声相机位姿下用于网格重建的鲁棒辐射场
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            RePose-NeRF: Robust Radiance Fields for Mesh Reconstruction under Noisy Camera Poses
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sriram Srinivasan, Gautam Ramachandra
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的神经辐射场（NeRF）和3D重建技术，属于纯粹的视觉领域研究。虽然涉及鲁棒性处理，但缺乏与推荐系统、搜索或广告领域的直接关联或潜在应用场景。论文内容主要围绕3D场景重建和相机位姿优化，属于您明确排除的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:25:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08545v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08545v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Accurate 3D reconstruction from multi-view images is essential for downstream robotic tasks such as navigation, manipulation, and environment understanding. However, obtaining precise camera poses in real-world settings remains challenging, even when calibration parameters are known. This limits the practicality of existing NeRF-based methods that rely heavily on accurate extrinsic estimates. Furthermore, their implicit volumetric representations differ significantly from the widely adopted polygonal meshes, making rendering and manipulation inefficient in standard 3D software. In this work, we propose a robust framework that reconstructs high-quality, editable 3D meshes directly from multi-view images with noisy extrinsic parameters. Our approach jointly refines camera poses while learning an implicit scene representation that captures fine geometric detail and photorealistic appearance. The resulting meshes are compatible with common 3D graphics and robotics tools, enabling efficient downstream use. Experiments on standard benchmarks demonstrate that our method achieves accurate and robust 3D reconstruction under pose uncertainty, bridging the gap between neural implicit representations and practical robotic applications.
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            <a href="https://www.alphaxiv.org/abs/2511.08465v1" target="_blank" rel="noopener noreferrer">
                基于统一数据集和Faster R-CNN的可泛化血细胞检测
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            Generalizable Blood Cell Detection via Unified Dataset and Faster R-CNN
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siddharth Sahay
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的血细胞检测，属于生物医学图像分析范畴，与推荐系统、搜索或广告没有任何技术关联。Faster R-CNN是计算机视觉中的目标检测方法，但该应用场景完全限定在医疗诊断领域，没有展示出在推荐、搜索或广告系统中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:08:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08465v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08465v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper presents a comprehensive methodology and comparative performance analysis for the automated classification and object detection of peripheral blood cells (PBCs) in microscopic images. Addressing the critical challenge of data scarcity and heterogeneity, robust data pipeline was first developed to standardize and merge four public datasets (PBC, BCCD, Chula, Sickle Cell) into a unified resource. Then employed a state-of-the-art Faster R-CNN object detection framework, leveraging a ResNet-50-FPN backbone. Comparative training rigorously evaluated a randomly initialized baseline model (Regimen 1) against a Transfer Learning Regimen (Regimen 2), initialized with weights pre-trained on the Microsoft COCO dataset. The results demonstrate that the Transfer Learning approach achieved significantly faster convergence and superior stability, culminating in a final validation loss of 0.08666, a substantial improvement over the baseline. This validated methodology establishes a robust foundation for building high-accuracy, deployable systems for automated hematological diagnosis.
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            <a href="https://www.alphaxiv.org/abs/2511.08464v1" target="_blank" rel="noopener noreferrer">
                对比集成梯度：一种基于特征归因的整张切片图像分类解释方法
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        <div class="mb-2 text-base text-gray-700">
            Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anh Mai Vu, Tuan L. Vo, Ngoc Lam Quang Bui, Nam Nguyen Le Binh, Akash Awasthi, H...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像（整张切片图像）分类的解释方法，属于计算机视觉在医疗领域的特定应用。虽然涉及特征归因技术，但该方法与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM或Transformer架构的进展。整张切片图像是医学病理学的专用格式，与RecSys/Search/Ads的异构数据处理需求无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:07:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08464v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08464v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These methods capture model decision patterns but may overlook class-discriminative signals that are crucial for distinguishing between tumor subtypes. In this work, we introduce Contrastive Integrated Gradients (CIG), a novel attribution method that enhances interpretability by computing contrastive gradients in logit space. First, CIG highlights class-discriminative regions by comparing feature importance relative to a reference class, offering sharper differentiation between tumor and non-tumor areas. Second, CIG satisfies the axioms of integrated attribution, ensuring consistency and theoretical soundness. Third, we propose two attribution quality metrics, MIL-AIC and MIL-SIC, which measure how predictive information and model confidence evolve with access to salient regions, particularly under weak supervision. We validate CIG across three datasets spanning distinct cancer types: CAMELYON16 (breast cancer metastasis in lymph nodes), TCGA-RCC (renal cell carcinoma), and TCGA-Lung (lung cancer). Experimental results demonstrate that CIG yields more informative attributions both quantitatively, using MIL-AIC and MIL-SIC, and qualitatively, through visualizations that align closely with ground truth tumor regions, underscoring its potential for interpretable and trustworthy WSI-based diagnostics
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            <a href="https://www.alphaxiv.org/abs/2511.07947v1" target="_blank" rel="noopener noreferrer">
                类别特征水印：一种针对模型提取攻击的鲁棒黑盒水印
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        <div class="mb-2 text-base text-gray-700">
            Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaxin Xiao, Qingqing Ye, Zi Liang, Haoyang Li, RongHua Li, Huadi Zheng, Haibo Hu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注模型水印和模型提取攻击防御，属于安全领域。虽然涉及模型保护，但这属于明确的无关主题（安全、隐私），与推荐系统、搜索、广告的核心技术进展或LLM应用无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:00:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07947v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07947v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through black-box queries. Model watermarking counters MEAs by embedding forensic markers for ownership verification. Current black-box watermarks prioritize MEA survival through representation entanglement, yet inadequately explore resilience against sequential MEAs and removal attacks. Our study reveals that this risk is underestimated because existing removal methods are weakened by entanglement. To address this gap, we propose Watermark Removal attacK (WRK), which circumvents entanglement constraints by exploiting decision boundaries shaped by prevailing sample-level watermark artifacts. WRK effectively reduces watermark success rates by at least 88.79% across existing watermarking benchmarks. For robust protection, we propose Class-Feature Watermarks (CFW), which improve resilience by leveraging class-level artifacts. CFW constructs a synthetic class using out-of-domain samples, eliminating vulnerable decision boundaries between original domain samples and their artifact-modified counterparts (watermark samples). CFW concurrently optimizes both MEA transferability and post-MEA stability. Experiments across multiple domains show that CFW consistently outperforms prior methods in resilience, maintaining a watermark success rate of at least 70.15% in extracted models even under the combined MEA and WRK distortion, while preserving the utility of protected models.
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            <a href="https://www.alphaxiv.org/abs/2511.07928v1" target="_blank" rel="noopener noreferrer">
                基于图像的路径规划算法：使用配备立体视觉的无人机
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            An Image-Based Path Planning Algorithm Using a UAV Equipped with Stereo Vision
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Selim Ahmet Iz, Mustafa Unel
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于无人机路径规划和计算机视觉应用，与搜索、推荐或广告系统完全无关。立体视觉和路径规划技术没有明显的潜力应用于推荐系统、搜索或广告领域，这超出了指定的关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:26:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07928v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07928v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    This paper presents a novel image-based path planning algorithm that was developed using computer vision techniques, as well as its comparative analysis with well-known deterministic and probabilistic algorithms, namely A* and Probabilistic Road Map algorithm (PRM). The terrain depth has a significant impact on the calculated path safety. The craters and hills on the surface cannot be distinguished in a two-dimensional image. The proposed method uses a disparity map of the terrain that is generated by using a UAV. Several computer vision techniques, including edge, line and corner detection methods, as well as the stereo depth reconstruction technique, are applied to the captured images and the found disparity map is used to define candidate way-points of the trajectory. The initial and desired points are detected automatically using ArUco marker pose estimation and circle detection techniques. After presenting the mathematical model and vision techniques, the developed algorithm is compared with well-known algorithms on different virtual scenes created in the V-REP simulation program and a physical setup created in a laboratory environment. Results are promising and demonstrate effectiveness of the proposed algorithm.
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            <a href="https://www.alphaxiv.org/abs/2511.08169v1" target="_blank" rel="noopener noreferrer">
                KPLM-STA：基于关键点的光照建模实现人体重照明的物理精确阴影合成
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            KPLM-STA: Physically-Accurate Shadow Synthesis for Human Relighting via Keypoint-Based Light Modeling
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinhui Yin, Qifei Li, Yilin Guo, Hongxia Xie, Xiaoli Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的阴影合成和人体重照明技术，属于纯粹的视觉计算领域。虽然涉及物理建模，但没有任何与推荐系统、搜索或广告相关的潜在应用场景或技术关联。论文的核心技术方向完全落在排除的视觉技术范畴内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:28:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08169v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08169v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image composition aims to seamlessly integrate a foreground object into a background, where generating realistic and geometrically accurate shadows remains a persistent challenge. While recent diffusion-based methods have outperformed GAN-based approaches, existing techniques, such as the diffusion-based relighting framework IC-Light, still fall short in producing shadows with both high appearance realism and geometric precision, especially in composite images. To address these limitations, we propose a novel shadow generation framework based on a Keypoints Linear Model (KPLM) and a Shadow Triangle Algorithm (STA). KPLM models articulated human bodies using nine keypoints and one bounding block, enabling physically plausible shadow projection and dynamic shading across joints, thereby enhancing visual realism. STA further improves geometric accuracy by computing shadow angles, lengths, and spatial positions through explicit geometric formulations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on shadow realism benchmarks, particularly under complex human poses, and generalizes effectively to multi-directional relighting scenarios such as those supported by IC-Light.
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            <a href="https://www.alphaxiv.org/abs/2511.08156v1" target="_blank" rel="noopener noreferrer">
                LandSegmenter：面向土地利用与土地覆盖制图的灵活基础模型
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            LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenying Liu, Wei Huang, Xiao Xiang Zhu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感图像的土地利用和土地覆盖分割，属于纯粹的计算机视觉应用领域。虽然标题中提到'基础模型'，但其应用场景（土地利用制图）与推荐系统、搜索或广告领域没有任何直接或间接的关联，完全超出了当前关注的技术范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 12:08:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08156v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08156v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a specific modality and a fixed class taxonomy, limiting their generability and broader applicability. Recent advances in foundation models (FMs) offer promising opportunities for building universal models. Yet, task-agnostic FMs often require fine-tuning for downstream applications, whereas task-specific FMs rely on massive amounts of labeled data for training, which is costly and impractical in the remote sensing (RS) domain. To address these challenges, we propose LandSegmenter, an LULC FM framework that resolves three-stage challenges at the input, model, and output levels. From the input side, to alleviate the heavy demand on labeled data for FM training, we introduce LAnd Segment (LAS), a large-scale, multi-modal, multi-source dataset built primarily with globally sampled weak labels from existing LULC products. LAS provides a scalable, cost-effective alternative to manual annotation, enabling large-scale FM training across diverse LULC domains. For model architecture, LandSegmenter integrates an RS-specific adapter for cross-modal feature extraction and a text encoder for semantic awareness enhancement. At the output stage, we introduce a class-wise confidence-guided fusion strategy to mitigate semantic omissions and further improve LandSegmenter's zero-shot performance. We evaluate LandSegmenter on six precisely annotated LULC datasets spanning diverse modalities and class taxonomies. Extensive transfer learning and zero-shot experiments demonstrate that LandSegmenter achieves competitive or superior performance, particularly in zero-shot settings when transferred to unseen datasets. These results highlight the efficacy of our proposed framework and the utility of weak supervision for building task-specific FMs.
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            <a href="https://www.alphaxiv.org/abs/2511.08071v1" target="_blank" rel="noopener noreferrer">
                Radar-APLANC：基于增强伪标签与噪声对比的无监督雷达心跳感知
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            Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ying Wang, Zhaodong Sun, Xu Cheng, Zuxian He, Xiaobai Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的雷达心跳监测技术，属于生物医学应用范畴。虽然涉及无监督学习和对比学习技术，但其核心应用场景（医疗监测）与推荐系统、搜索或广告领域完全无关，且不涉及任何LLM、Transformer架构或异构数据建模技术。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:14:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08071v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08071v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span><span class="category-tag">eess.SP</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Frequency Modulated Continuous Wave (FMCW) radars can measure subtle chest wall oscillations to enable non-contact heartbeat sensing. However, traditional radar-based heartbeat sensing methods face performance degradation due to noise. Learning-based radar methods achieve better noise robustness but require costly labeled signals for supervised training. To overcome these limitations, we propose the first unsupervised framework for radar-based heartbeat sensing via Augmented Pseudo-Label and Noise Contrast (Radar-APLANC). We propose to use both the heartbeat range and noise range within the radar range matrix to construct the positive and negative samples, respectively, for improved noise robustness. Our Noise-Contrastive Triplet (NCT) loss only utilizes positive samples, negative samples, and pseudo-label signals generated by the traditional radar method, thereby avoiding dependence on expensive ground-truth physiological signals. We further design a pseudo-label augmentation approach featuring adaptive noise-aware label selection to improve pseudo-label signal quality. Extensive experiments on the Equipleth dataset and our collected radar dataset demonstrate that our unsupervised method achieves performance comparable to state-of-the-art supervised methods. Our code, dataset, and supplementary materials can be accessed from https://github.com/RadarHRSensing/Radar-APLANC.
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            <a href="https://www.alphaxiv.org/abs/2511.08065v1" target="_blank" rel="noopener noreferrer">
                I2E：面向高性能脉冲神经网络的高效实时图像到事件转换
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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            I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruichen Ma, Liwei Meng, Guanchao Qiao, Ning Ning, Yang Liu, Shaogang Hu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脉冲神经网络（SNN）和图像到事件转换技术，属于神经形态计算和计算机视觉领域。虽然提到了高效处理，但论文内容与推荐系统、搜索、广告或相关使能技术没有明确关联，也不涉及Transformer架构或LLM在多模态数据处理中的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 10:05:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08065v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08065v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data. This work introduces I2E, an algorithmic framework that resolves this bottleneck by converting static images into high-fidelity event streams. By simulating microsaccadic eye movements with a highly parallelized convolution, I2E achieves a conversion speed over 300x faster than prior methods, uniquely enabling on-the-fly data augmentation for SNN training. The framework's effectiveness is demonstrated on large-scale benchmarks. An SNN trained on the generated I2E-ImageNet dataset achieves a state-of-the-art accuracy of 60.50%. Critically, this work establishes a powerful sim-to-real paradigm where pre-training on synthetic I2E data and fine-tuning on the real-world CIFAR10-DVS dataset yields an unprecedented accuracy of 92.5%. This result validates that synthetic event data can serve as a high-fidelity proxy for real sensor data, bridging a long-standing gap in neuromorphic engineering. By providing a scalable solution to the data problem, I2E offers a foundational toolkit for developing high-performance neuromorphic systems. The open-source algorithm and all generated datasets are provided to accelerate research in the field.
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            <a href="https://www.alphaxiv.org/abs/2511.08046v1" target="_blank" rel="noopener noreferrer">
                ProSona：基于提示引导的多专家医学图像分割个性化方法
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aya Elgebaly, Nikolaos Delopoulos, Juliane Hörner-Rieber, Carolin Rippke, Sebast...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分割领域，属于明确的医学应用范畴，与推荐系统、搜索或广告无关。论文标题中提到的个性化方法虽然听起来相关，但完全限定在医学图像处理场景，没有任何技术元素或潜在应用可以迁移到RecSys/Search/Ads领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 09:50:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08046v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08046v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Automated medical image segmentation suffers from high inter-observer variability, particularly in tasks such as lung nodule delineation, where experts often disagree. Existing approaches either collapse this variability into a consensus mask or rely on separate model branches for each annotator. We introduce ProSona, a two-stage framework that learns a continuous latent space of annotation styles, enabling controllable personalization via natural language prompts. A probabilistic U-Net backbone captures diverse expert hypotheses, while a prompt-guided projection mechanism navigates this latent space to generate personalized segmentations. A multi-level contrastive objective aligns textual and visual representations, promoting disentangled and interpretable expert styles. Across the LIDC-IDRI lung nodule and multi-institutional prostate MRI datasets, ProSona reduces the Generalized Energy Distance by 17% and improves mean Dice by more than one point compared with DPersona. These results demonstrate that natural-language prompts can provide flexible, accurate, and interpretable control over personalized medical image segmentation. Our implementation is available online 1 .
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            <a href="https://www.alphaxiv.org/abs/2511.07978v1" target="_blank" rel="noopener noreferrer">
                DANCE：用于点云补全的密度无关且类别感知网络
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            DANCE: Density-agnostic and Class-aware Network for Point Cloud Completion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Da-Yeong Kim, Yeong-Jun Cho
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D点云补全技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告没有直接关联。点云处理技术主要应用于自动驾驶、机器人感知和3D建模等场景，缺乏在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 08:45:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07978v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07978v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Point cloud completion aims to recover missing geometric structures from incomplete 3D scans, which often suffer from occlusions or limited sensor viewpoints. Existing methods typically assume fixed input/output densities or rely on image-based representations, making them less suitable for real-world scenarios with variable sparsity and limited supervision. In this paper, we introduce Density-agnostic and Class-aware Network (DANCE), a novel framework that completes only the missing regions while preserving the observed geometry. DANCE generates candidate points via ray-based sampling from multiple viewpoints. A transformer decoder then refines their positions and predicts opacity scores, which determine the validity of each point for inclusion in the final surface. To incorporate semantic guidance, a lightweight classification head is trained directly on geometric features, enabling category-consistent completion without external image supervision. Extensive experiments on the PCN and MVP benchmarks show that DANCE outperforms state-of-the-art methods in accuracy and structural consistency, while remaining robust to varying input densities and noise levels.
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            <a href="https://www.alphaxiv.org/abs/2511.08387v1" target="_blank" rel="noopener noreferrer">
                RAPTR：基于雷达并使用Transformer的3D姿态估计
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            RAPTR: Radar-based 3D Pose Estimation using Transformer
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sorachi Kato, Ryoma Yataka, Pu Perry Wang, Pedro Miraldo, Takuya Fujihashi, Petr...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于雷达传感器和3D姿态估计的计算机视觉任务，属于纯粹的视觉/传感器领域应用。Transformer在此处仅作为通用架构组件，没有展示在推荐系统、搜索或广告领域的潜在应用价值。论文内容与异构数据建模、推荐算法或搜索排序等核心关注领域完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:07:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08387v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08387v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">eess.SP</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint labels which are considerably easier and more scalable to collect. Our RAPTR is characterized by a two-stage pose decoder architecture with a pseudo-3D deformable attention to enhance (pose/joint) queries with multi-view radar features: a pose decoder estimates initial 3D poses with a 3D template loss designed to utilize the 3D BBox labels and mitigate depth ambiguities; and a joint decoder refines the initial poses with 2D keypoint labels and a 3D gravity loss. Evaluated on two indoor radar datasets, RAPTR outperforms existing methods, reducing joint position error by $34.3\%$ on HIBER and $76.9\%$ on MMVR. Our implementation is available at https://github.com/merlresearch/radar-pose-transformer.
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            <a href="https://www.alphaxiv.org/abs/2511.08233v1" target="_blank" rel="noopener noreferrer">
                通过几何感知的局部自适应实现从点云的精确高效表面重建
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            Accurate and Efficient Surface Reconstruction from Point Clouds via Geometry-Aware Local Adaptation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Eito Ogawa, Taiga Hayami, Hiroshi Watanabe
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的3D表面重建技术，属于纯粹的几何处理和3D视觉领域。虽然点云处理在技术上很先进，但该工作没有展示与推荐系统、搜索或广告应用的明确关联，也不涉及LLM、Transformer架构或异构数据建模等焦点领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 13:32:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08233v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08233v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Point cloud surface reconstruction has improved in accuracy with advances in deep learning, enabling applications such as infrastructure inspection. Recent approaches that reconstruct from small local regions rather than entire point clouds have attracted attention for their strong generalization capability. However, prior work typically places local regions uniformly and keeps their size fixed, limiting adaptability to variations in geometric complexity. In this study, we propose a method that improves reconstruction accuracy and efficiency by adaptively modulating the spacing and size of local regions based on the curvature of the input point cloud.
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            <a href="https://www.alphaxiv.org/abs/2511.08536v1" target="_blank" rel="noopener noreferrer">
                3D4D：通过3D视频生成实现的交互式、可编辑4D世界模型
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            3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunhong He, Zhengqing Yuan, Zhengzhong Tu, Yanfang Ye, Lichao Sun
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D和4D世界生成技术，属于计算机视觉和图形学领域。虽然提到了交互式和可编辑模型，但核心内容围绕3D视频生成和4D世界建模，与推荐系统、搜索或广告没有直接关联。该技术主要面向虚拟现实、游戏或3D内容创作，而非排名、检索或个性化推荐等核心业务场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:16:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08536v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08536v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce 3D4D, an interactive 4D visualization framework that integrates WebGL with Supersplat rendering. It transforms static images and text into coherent 4D scenes through four core modules and employs a foveated rendering strategy for efficient, real-time multi-modal interaction. This framework enables adaptive, user-driven exploration of complex 4D environments. The project page and code are available at https://yunhonghe1021.github.io/NOVA/.
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            <a href="https://www.alphaxiv.org/abs/2511.08535v1" target="_blank" rel="noopener noreferrer">
                大规模手语模型：面向3D美国手语翻译
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Large Sign Language Models: Toward 3D American Sign Language Translation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sen Zhang, Xiaoxiao He, Di Liu, Zhaoyang Xia, Mingyu Zhao, Chaowei Tan, Vivian L...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D手语翻译这一特定领域应用，与搜索、推荐或广告系统无直接关联。手语翻译属于专门的模态转换任务，不涉及用户行为建模、内容排序或商业场景中的信息检索等核心关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 18:16:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08535v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08535v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    We present Large Sign Language Models (LSLM), a novel framework for translating 3D American Sign Language (ASL) by leveraging Large Language Models (LLMs) as the backbone, which can benefit hearing-impaired individuals' virtual communication. Unlike existing sign language recognition methods that rely on 2D video, our approach directly utilizes 3D sign language data to capture rich spatial, gestural, and depth information in 3D scenes. This enables more accurate and resilient translation, enhancing digital communication accessibility for the hearing-impaired community. Beyond the task of ASL translation, our work explores the integration of complex, embodied multimodal languages into the processing capabilities of LLMs, moving beyond purely text-based inputs to broaden their understanding of human communication. We investigate both direct translation from 3D gesture features to text and an instruction-guided setting where translations can be modulated by external prompts, offering greater flexibility. This work provides a foundational step toward inclusive, multimodal intelligent systems capable of understanding diverse forms of language.
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            <a href="https://www.alphaxiv.org/abs/2511.08509v1" target="_blank" rel="noopener noreferrer">
                基于层次稀疏采样与残差Transformer的CT图像中多器官快速精细分割
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        <div class="mb-2 text-base text-gray-700">
            Fast Multi-Organ Fine Segmentation in CT Images with Hierarchical Sparse Sampling and Residual Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xueqi Guo, Halid Ziya Yerebakan, Yoshihisa Shinagawa, Kritika Iyer, Gerardo Herm...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像分割领域，特别是CT图像中的多器官分割，这属于医学和生物医学应用的范畴。尽管使用了Transformer架构，但该工作纯粹针对医疗领域，与推荐系统、搜索或广告没有任何直接或间接的关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 17:43:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08509v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08509v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multi-organ segmentation of 3D medical images is fundamental with meaningful applications in various clinical automation pipelines. Although deep learning has achieved superior performance, the time and memory consumption of segmenting the entire 3D volume voxel by voxel using neural networks can be huge. Classifiers have been developed as an alternative in cases with certain points of interest, but the trade-off between speed and accuracy remains an issue. Thus, we propose a novel fast multi-organ segmentation framework with the usage of hierarchical sparse sampling and a Residual Transformer. Compared with whole-volume analysis, the hierarchical sparse sampling strategy could successfully reduce computation time while preserving a meaningful hierarchical context utilizing multiple resolution levels. The architecture of the Residual Transformer segmentation network could extract and combine information from different levels of information in the sparse descriptor while maintaining a low computational cost. In an internal data set containing 10,253 CT images and the public dataset TotalSegmentator, the proposed method successfully improved qualitative and quantitative segmentation performance compared to the current fast organ classifier, with fast speed at the level of ~2.24 seconds on CPU hardware. The potential of achieving real-time fine organ segmentation is suggested.
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            <a href="https://www.alphaxiv.org/abs/2511.08402v1" target="_blank" rel="noopener noreferrer">
                Anatomy-VLM：用于医学解释的细粒度视觉语言模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Difei Gu, Yunhe Gao, Mu Zhou, Dimitris Metaxas
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的视觉语言模型应用，属于明确的医学领域特定应用，这在无关主题中被明确排除。虽然论文涉及视觉语言模型技术，但其医学解释的特定应用场景与推荐系统、搜索或广告领域没有直接关联，也不符合VLM类比异构数据的关注点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 16:18:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08402v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08402v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image features with clinical knowledge. Yet major vision-language models (VLMs) treat images as holistic entities and overlook fine-grained image details that are vital for disease diagnosis. Clinicians analyze images by utilizing their prior medical knowledge and identify anatomical structures as important region of interests (ROIs). Inspired from this human-centric workflow, we introduce Anatomy-VLM, a fine-grained, vision-language model that incorporates multi-scale information. First, we design a model encoder to localize key anatomical features from entire medical images. Second, these regions are enriched with structured knowledge for contextually-aware interpretation. Finally, the model encoder aligns multi-scale medical information to generate clinically-interpretable disease prediction. Anatomy-VLM achieves outstanding performance on both in- and out-of-distribution datasets. We also validate the performance of Anatomy-VLM on downstream image segmentation tasks, suggesting that its fine-grained alignment captures anatomical and pathology-related knowledge. Furthermore, the Anatomy-VLM's encoder facilitates zero-shot anatomy-wise interpretation, providing its strong expert-level clinical interpretation capabilities.
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            <a href="https://www.alphaxiv.org/abs/2511.08369v1" target="_blank" rel="noopener noreferrer">
                基于文本的空中-地面人员检索
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Text-based Aerial-Ground Person Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyu Zhou, Yu Wu, Jiayao Ma, Wenhao Wang, Min Cao, Mang Ye
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的跨模态检索任务，涉及无人机视角与地面视角的人员匹配，属于纯粹的视觉应用范畴。该研究没有展示与推荐系统、搜索或广告相关的潜在应用，也不涉及LLM技术、Transformer架构进展或异构数据统一建模等当前关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:49:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08369v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08369v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    This work introduces Text-based Aerial-Ground Person Retrieval (TAG-PR), which aims to retrieve person images from heterogeneous aerial and ground views with textual descriptions. Unlike traditional Text-based Person Retrieval (T-PR), which focuses solely on ground-view images, TAG-PR introduces greater practical significance and presents unique challenges due to the large viewpoint discrepancy across images. To support this task, we contribute: (1) TAG-PEDES dataset, constructed from public benchmarks with automatically generated textual descriptions, enhanced by a diversified text generation paradigm to ensure robustness under view heterogeneity; and (2) TAG-CLIP, a novel retrieval framework that addresses view heterogeneity through a hierarchically-routed mixture of experts module to learn view-specific and view-agnostic features and a viewpoint decoupling strategy to decouple view-specific features for better cross-modal alignment. We evaluate the effectiveness of TAG-CLIP on both the proposed TAG-PEDES dataset and existing T-PR benchmarks. The dataset and code are available at https://github.com/Flame-Chasers/TAG-PR.
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            <a href="https://www.alphaxiv.org/abs/2511.08328v1" target="_blank" rel="noopener noreferrer">
                纵向乳腺X光片对齐对乳腺癌风险评估的影响
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            The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba A. Salahuddin, Xin Wan...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学影像分析中的乳腺癌风险评估，属于明确的医学领域应用。虽然涉及纵向数据对齐技术，但该技术特定于乳腺X光片分析，与推荐系统、搜索或广告领域没有任何直接或潜在的应用关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 15:03:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08328v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08328v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can be personalized, especially for high-risk individuals. While recent methods increasingly incorporate longitudinal information from prior mammograms, accurate spatial alignment across time points remains a key challenge. Misalignment can obscure meaningful tissue changes and degrade model performance. In this study, we provide insights into various alignment strategies, image-based registration, feature-level (representation space) alignment with and without regularization, and implicit alignment methods, for their effectiveness in longitudinal deep learning-based risk modeling. Using two large-scale mammography datasets, we assess each method across key metrics, including predictive accuracy, precision, recall, and deformation field quality. Our results show that image-based registration consistently outperforms the more recently favored feature-based and implicit approaches across all metrics, enabling more accurate, temporally consistent predictions and generating smooth, anatomically plausible deformation fields. Although regularizing the deformation field improves deformation quality, it reduces the risk prediction performance of feature-level alignment. Applying image-based deformation fields within the feature space yields the best risk prediction performance. These findings underscore the importance of image-based deformation fields for spatial alignment in longitudinal risk modeling, offering improved prediction accuracy and robustness. This approach has strong potential to enhance personalized screening and enable earlier interventions for high-risk individuals. The code is available at https://github.com/sot176/Mammogram_Alignment_Study_Risk_Prediction.git, allowing full reproducibility of the results.
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            <a href="https://www.alphaxiv.org/abs/2511.08294v1" target="_blank" rel="noopener noreferrer">
                SkelSplat：基于可微分高斯渲染的鲁棒多视角3D人体姿态估计
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            SkelSplat: Robust Multi-view 3D Human Pose Estimation with Differentiable Gaussian Rendering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Laura Bragagnolo, Leonardo Barcellona, Stefano Ghidoni
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多视角3D人体姿态估计和可微分渲染技术，属于计算机视觉领域。虽然涉及3D重建和姿态估计，但这些技术与搜索、推荐或广告系统的核心需求（如用户行为建模、内容理解、个性化排序）没有直接关联。该工作主要面向人体动作分析应用，不符合当前关注的LLM技术、推荐系统核心进展或Transformer架构改进等方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 14:28:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.08294v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.08294v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Accurate 3D human pose estimation is fundamental for applications such as augmented reality and human-robot interaction. State-of-the-art multi-view methods learn to fuse predictions across views by training on large annotated datasets, leading to poor generalization when the test scenario differs. To overcome these limitations, we propose SkelSplat, a novel framework for multi-view 3D human pose estimation based on differentiable Gaussian rendering. Human pose is modeled as a skeleton of 3D Gaussians, one per joint, optimized via differentiable rendering to enable seamless fusion of arbitrary camera views without 3D ground-truth supervision. Since Gaussian Splatting was originally designed for dense scene reconstruction, we propose a novel one-hot encoding scheme that enables independent optimization of human joints. SkelSplat outperforms approaches that do not rely on 3D ground truth in Human3.6M and CMU, while reducing the cross-dataset error up to 47.8% compared to learning-based methods. Experiments on Human3.6M-Occ and Occlusion-Person demonstrate robustness to occlusions, without scenario-specific fine-tuning. Our project page is available here: https://skelsplat.github.io.
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            <a href="https://www.alphaxiv.org/abs/2511.07926v1" target="_blank" rel="noopener noreferrer">
                基于CNN的忆阻器件建模自动化参数提取框架
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            CNN-Based Automated Parameter Extraction Framework for Modeling Memristive Devices
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Akif Hamid, Orchi Hassan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于忆阻器件的参数提取和建模，属于硬件和电子器件领域。虽然CNN是深度学习技术，但论文的应用场景是物理器件建模，与推荐系统、搜索或广告的核心技术栈没有直接关联。该研究缺乏在推荐、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 07:24:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07926v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07926v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.ET</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Resistive random access memory (RRAM) is a promising candidate for next-generation nonvolatile memory (NVM) and in-memory computing applications. Compact models are essential for analyzing the circuit and system-level performance of experimental RRAM devices. However, most existing RRAM compact models rely on multiple fitting parameters to reproduce the device I-V characteristics, and in most cases, as the parameters are not directly related to measurable quantities, their extraction requires extensive manual tuning, making the process time-consuming and limiting adaptability across different devices. This work presents an automated framework for extracting the fitting parameters of the widely used Stanford RRAM model directly from the device I-V characteristics. The framework employs a convolutional neural network (CNN) trained on a synthetic dataset to generate initial parameter estimates, which are then refined through three heuristic optimization blocks that minimize errors via adaptive binary search in the parameter space. We evaluated the framework using four key NVM metrics: set voltage, reset voltage, hysteresis loop area, and low resistance state (LRS) slope. Benchmarking against RRAM device characteristics derived from previously reported Stanford model fits, other analytical models, and experimental data shows that the framework achieves low error across diverse device characteristics, offering a fast, reliable, and robust solution for RRAM modeling.
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            <a href="https://www.alphaxiv.org/abs/2511.07827v1" target="_blank" rel="noopener noreferrer">
                产前超声深度学习分析用于脑室扩大识别
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            Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Youssef Megahed, Inok Lee, Robin Ducharme, Aylin Erman, Olivier X. Miguel, Kevin...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像分析（产前超声）和特定医疗状况（脑室扩大）的检测，这属于明确的医学领域应用。论文内容与推荐系统、搜索、广告或相关使能技术没有任何关联，完全超出了用户关注的核心技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 04:45:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07827v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07827v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span></div>
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                    The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying genetic syndromes. An Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), recently developed by our group, was fine-tuned for a binary classification task to distinguish fetal brain ultrasound images as either normal or showing ventriculomegaly. The USF-MAE incorporates a Vision Transformer encoder pretrained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, the pretrained encoder was adapted and fine-tuned on a curated dataset of fetal brain ultrasound images to optimize its performance for ventriculomegaly detection. Model evaluation was conducted using 5-fold cross-validation and an independent test cohort, and performance was quantified using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed USF-MAE model reached an F1-score of 91.76% on the 5-fold cross-validation and 91.78% on the independent test set, with much higher scores than those obtained by the baseline models by 19.37% and 16.15% compared to VGG-19, 2.31% and 2.56% compared to ResNet-50, and 5.03% and 11.93% compared to ViT-B/16, respectively. The model also showed a high mean test precision of 94.47% and an accuracy of 97.24%. The Eigen-CAM (Eigen Class Activation Map) heatmaps showed that the model was focusing on the ventricle area for the diagnosis of ventriculomegaly, which has explainability and clinical plausibility.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.07801v1" target="_blank" rel="noopener noreferrer">
                用于多标签胸部X光诊断的稀疏标签耦合学习
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Learning Sparse Label Couplings for Multilabel Chest X-Ray Diagnosis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Utkarsh Prakash Srivastava, Kaushik Gupta, Kaushik Nath
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像诊断（胸部X光），这属于明确的无关主题范畴（医学/生物学领域特定应用）。论文内容涉及多标签分类和标签耦合学习，与推荐系统、搜索或广告的核心领域进展、LLM技术或Transformer架构改进没有任何关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-11 03:38:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.07801v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.07801v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We study multilabel classification of chest X-rays and present a simple, strong pipeline built on SE-ResNeXt101 $(32 \times 4d)$. The backbone is finetuned for 14 thoracic findings with a sigmoid head, trained using Multilabel Iterative Stratification (MIS) for robust cross-validation splits that preserve label co-occurrence. To address extreme class imbalance and asymmetric error costs, we optimize with Asymmetric Loss, employ mixed-precision (AMP), cosine learning-rate decay with warm-up, gradient clipping, and an exponential moving average (EMA) of weights. We propose a lightweight Label-Graph Refinement module placed after the classifier: given per-label probabilities, it learns a sparse, trainable inter-label coupling matrix that refines logits via a single message-passing step while adding only an L1-regularized parameter head. At inference, we apply horizontal flip test-time augmentation (TTA) and average predictions across MIS folds (a compact deep ensemble). Evaluation uses macro AUC averaging classwise ROC-AUC and skipping single-class labels in a fold to reflect balanced performance across conditions. On our dataset, a strong SE-ResNeXt101 baseline attains competitive macro AUC (e.g., 92.64% in our runs). Adding the Label-Graph Refinement consistently improves validation macro AUC across folds with negligible compute. The resulting method is reproducible, hardware-friendly, and requires no extra annotations, offering a practical route to stronger multilabel CXR classifiers.
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        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251105","20251107","20251009","20251030","20251111","20251031","20251017","20251021","20251010","20251024","20251022","20251029","20251016","20251015","20251028","20251014","20251112","20251106","20251023"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>